{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Output/Results1.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}25 Jul 2020, 20:32:59
{txt}
{com}. 
. 
. *** Code to generate the analysis data and run summary statistics (Table A-1) ****
. do "$localdir/Code/generate_analysis_data.do"
{txt}
{com}. 
. /*
> ********* This program cleans survey data for all cities, renames and recodes variables, 
> ********* generates labels, and saves a final analysis dataset 
> ********* The program also generates summary statistics in Table A-1 of the appendix 
> */
. 
. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. cd "$localdir/Data"
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Data
{txt}
{com}. gl output "$localdir/Output"
{txt}
{com}. 
. 
. *** Load raw data
. use "STAN0107_OUTPUT_8msas.dta", clear
{txt}( )

{com}. 
. foreach x of varlist _all {c -(}
{txt}  2{com}. capture rename `x' `=lower("`x'")'
{txt}  3{com}. {c )-}
{txt}
{com}. 
. 
. 
. *** Rename and recode variables and make value labels
. 
. * Economic evaluations and beliefs
. rename q1_all dissatisfied_us_economy
{res}{txt}
{com}. rename q2_all dissatisfied_msa_economy
{res}{txt}
{com}. rename q3_all dissatisfied_personal_finances
{res}{txt}
{com}. drop q1 q1_rev q2 q2_rev q3 q3_rev
{txt}
{com}. rename q4 better_off_than_parents
{res}{txt}
{com}. recode better_off_than_parents 1=5 2=4 3=3 4=2 5=1
{txt}(better_off_than_parents: 6176 changes made)

{com}. lab define agree 1 "Disagree Strongly" 2 "Disagree Somewhat" 3 "Neither Agree nor Disagree" 4 "Agree Somewhat" 5 "Agree Strongly"
{txt}
{com}. lab val better_off_than_parents agree
{txt}
{com}. rename q5 children_better_pros
{res}{txt}
{com}. recode children_better_pros 1=5 2=4 3=3 4=2 5=1
{txt}(children_better_pros: 5488 changes made)

{com}. lab val children_better_pros agree
{txt}
{com}. rename q6 changes_msa_worse
{res}{txt}
{com}. rename q10 most_imp_techortrade_msa
{res}{txt}
{com}. rename q11 most_upset_techortrade_msa
{res}{txt}
{com}. rename q12 hard_work_vs_luck_now
{res}{txt}
{com}. rename q13 hard_work_vs_luck_then
{res}{txt}
{com}. rename q14 policy_approach_trade
{res}{txt}
{com}. rename q15 policy_approach_tech
{res}{txt}
{com}. rename q17 inequality_smaller
{res}{txt}
{com}. recode inequality_smaller 1=3 3=2 2=1
{txt}(inequality_smaller: 7800 changes made)

{com}. lab define ineqsmall 1 "Bigger" 2 "About what it is now" 3 "Smaller" 
{txt}
{com}. lab values inequality_smaller ineqsmall
{txt}
{com}. rename q18 gov_more_safety_net
{res}{txt}
{com}. recode gov_more_safety_net 1=5 2=4 4=2 5=1
{txt}(gov_more_safety_net: 5641 changes made)

{com}. lab define safety 1 "Spend much less" 2 "Spend less" 3 "Spend the same" 4 "Spend more" 5 "Spend much more"
{txt}
{com}. lab values gov_more_safety_net safety
{txt}
{com}. rename q19 reduce_trade
{res}{txt}
{com}. recode reduce_trade 6=.
{txt}(reduce_trade: 760 changes made)

{com}. rename q20 for_investment
{res}{txt}
{com}. rename q21 reduce_immig
{res}{txt}
{com}. recode reduce_immig 1=1 2=2 3=3 4=5 5=4  
{txt}(reduce_immig: 3228 changes made)

{com}. lab define immigop 1 "Increased a lot" 2 "Increased a little" 3 "Remain the same as it is" 4 "Reduced a little" 5 "Reduced a lot"
{txt}
{com}. lab values reduce_immig immigop
{txt}
{com}. gen attentioncheck=0
{txt}
{com}. replace attentioncheck=1 if q22_test_2==1 & q22_test_3==1 
{txt}(6,947 real changes made)

{com}. rename q23 country_dissatisfied
{res}{txt}
{com}. recode country_dissatisfied 1=0 2=1 
{txt}(country_dissatisfied: 7800 changes made)

{com}. lab def satisfaction 0 "Satisfied" 1 "Dissatisfied"
{txt}
{com}. lab val country_dissatisfied satisfaction
{txt}
{com}. rename q24 state_dissatisfied
{res}{txt}
{com}. recode state_dissatisfied 1=0 2=1 
{txt}(state_dissatisfied: 7800 changes made)

{com}. lab val state_dissatisfied satisfaction
{txt}
{com}. rename q25 msa_dissatisfied
{res}{txt}
{com}. recode msa_dissatisfied 1=0 2=1 
{txt}(msa_dissatisfied: 7800 changes made)

{com}. lab val msa_dissatisfied satisfaction
{txt}
{com}. 
. * Performance factors
. rename q7a imp_perf_factor_trade
{res}{txt}
{com}. recode imp_perf_factor_trade 1=4 2=3 3=2 4=1 
{txt}(imp_perf_factor_trade: 7800 changes made)

{com}. lab define vimp 1 "Not Important At all" 2 "Of Little Importance" 3 "Somewhat Important" 4 "Very Important"
{txt}
{com}. lab values imp_perf_factor_trade vimp
{txt}
{com}. 
. rename q7b imp_perf_factor_tech_change
{res}{txt}
{com}. recode imp_perf_factor_tech_change 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_tech_change: 7800 changes made)

{com}. lab values imp_perf_factor_tech_change vimp
{txt}
{com}. 
. rename q7c imp_perf_factor_labor_unions
{res}{txt}
{com}. recode imp_perf_factor_labor_unions 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_labor_unions: 7800 changes made)

{com}. lab values imp_perf_factor_labor_unions vimp
{txt}
{com}. 
. rename q7d imp_perf_factor_bus_leaders
{res}{txt}
{com}. recode imp_perf_factor_bus_leaders 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_bus_leaders: 7800 changes made)

{com}. lab values imp_perf_factor_bus_leaders vimp
{txt}
{com}. 
. rename q7e imp_perf_factor_bankers
{res}{txt}
{com}. recode imp_perf_factor_bankers 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_bankers: 7800 changes made)

{com}. lab values imp_perf_factor_bankers vimp
{txt}
{com}. 
. rename q7f imp_perf_factor_leaders_wash
{res}{txt}
{com}. recode imp_perf_factor_leaders_wash 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_leaders_wash: 7800 changes made)

{com}. lab values imp_perf_factor_leaders_wash vimp
{txt}
{com}. 
. rename q7g imp_perf_factor_leaders_msa
{res}{txt}
{com}. recode imp_perf_factor_leaders_msa 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_leaders_msa: 7800 changes made)

{com}. lab values imp_perf_factor_leaders_msa vimp
{txt}
{com}. 
. rename q7h imp_perf_factor_mergers
{res}{txt}
{com}. recode imp_perf_factor_mergers 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_mergers: 7800 changes made)

{com}. lab values imp_perf_factor_mergers vimp
{txt}
{com}. 
. rename q7i imp_perf_factor_localecpolicy
{res}{txt}
{com}. recode imp_perf_factor_localecpolicy 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_localecpolicy: 7800 changes made)

{com}. lab values imp_perf_factor_localecpolicy vimp
{txt}
{com}. 
. rename q7j imp_perf_factor_localedpolicy
{res}{txt}
{com}. recode imp_perf_factor_localedpolicy 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_localedpolicy: 7800 changes made)

{com}. lab values imp_perf_factor_localedpolicy vimp
{txt}
{com}. 
. rename q7k imp_perf_factor_localcharities
{res}{txt}
{com}. recode imp_perf_factor_localcharities 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_localcharities: 7800 changes made)

{com}. lab values imp_perf_factor_localcharities vimp
{txt}
{com}. 
. rename q7l imp_perf_factor_localhighered
{res}{txt}
{com}. recode imp_perf_factor_localhighered 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_localhighered: 7800 changes made)

{com}. lab values imp_perf_factor_localhighered vimp
{txt}
{com}. 
. rename q7m imp_perf_factor_nationalecpolicy
{res}{txt}
{com}. recode imp_perf_factor_nationalecpolicy 1=4 2=3 3=2 4=1
{txt}(imp_perf_factor_nationalecpolicy: 7800 changes made)

{com}. lab values imp_perf_factor_nationalecpolicy vimp
{txt}
{com}. 
. rename q8 most_important_factor_msa
{res}{txt}
{com}. 
. rename q9 reason_most_imp_factor_text
{res}{txt}
{com}. 
. * Trust
. rename q26 trust_people
{res}{txt}
{com}. recode trust_people 1=3 2=2 3=1
{txt}(trust_people: 4377 changes made)

{com}. lab define trustp 1 "Only some of the time" 2 "Most of the time" 3 "Just about always" 
{txt}
{com}. lab values trust_people trustp
{txt}
{com}. rename q27a trust_democraticparty
{res}{txt}
{com}. recode trust_democraticparty 1=4 2=3 3=2 4=1
{txt}(trust_democraticparty: 7800 changes made)

{com}. lab define trust 1 "Trust them not at all" 2 "Trust them only a little" 3 "Trust them some" 4 "Trust them a lot" 
{txt}
{com}. lab values trust_democraticparty trust
{txt}
{com}. rename q27b trust_republicanparty
{res}{txt}
{com}. recode trust_republicanparty 1=4 2=3 3=2 4=1
{txt}(trust_republicanparty: 7800 changes made)

{com}. lab values trust_republicanparty trust
{txt}
{com}. rename q27c trust_polleaders
{res}{txt}
{com}. recode trust_polleaders 1=4 2=3 3=2 4=1
{txt}(trust_polleaders: 7800 changes made)

{com}. lab values trust_polleaders trust
{txt}
{com}. rename q27d trust_businessleaders
{res}{txt}
{com}. recode trust_businessleaders 1=4 2=3 3=2 4=1
{txt}(trust_businessleaders: 7800 changes made)

{com}. lab values trust_businessleaders trust
{txt}
{com}. rename q27e trust_unionleaders
{res}{txt}
{com}. recode trust_unionleaders 1=4 2=3 3=2 4=1
{txt}(trust_unionleaders: 7800 changes made)

{com}. lab values trust_unionleaders trust
{txt}
{com}. rename q27f trust_companies
{res}{txt}
{com}. recode trust_companies 1=4 2=3 3=2 4=1
{txt}(trust_companies: 7800 changes made)

{com}. lab values trust_companies trust
{txt}
{com}. rename q27g trust_whites
{res}{txt}
{com}. recode trust_whites 1=4 2=3 3=2 4=1
{txt}(trust_whites: 7800 changes made)

{com}. lab values trust_whites trust
{txt}
{com}. rename q27h trust_blacks
{res}{txt}
{com}. recode trust_blacks 1=4 2=3 3=2 4=1
{txt}(trust_blacks: 7800 changes made)

{com}. lab values trust_blacks trust
{txt}
{com}. rename q27i trust_latinos
{res}{txt}
{com}. recode trust_latinos 1=4 2=3 3=2 4=1
{txt}(trust_latinos: 7800 changes made)

{com}. lab values trust_latinos trust
{txt}
{com}. rename q27j trust_localgovMsa
{res}{txt}
{com}. recode trust_localgovMsa 1=4 2=3 3=2 4=1
{txt}(trust_localgovMsa: 7800 changes made)

{com}. lab values trust_localgovMsa trust
{txt}
{com}. rename q27k trust_stategov
{res}{txt}
{com}. recode trust_stategov 1=4 2=3 3=2 4=1
{txt}(trust_stategov: 7800 changes made)

{com}. lab values trust_stategov trust
{txt}
{com}. rename q27l trust_fedgov
{res}{txt}
{com}. recode trust_fedgov 1=4 2=3 3=2 4=1
{txt}(trust_fedgov: 7800 changes made)

{com}. lab values trust_fedgov trust
{txt}
{com}. rename q27m trust_localschools
{res}{txt}
{com}. recode trust_localschools 1=4 2=3 3=2 4=1
{txt}(trust_localschools: 7800 changes made)

{com}. lab values trust_localschools trust
{txt}
{com}. rename q27n trust_localcharities
{res}{txt}
{com}. recode trust_localcharities 1=4 2=3 3=2 4=1
{txt}(trust_localcharities: 7800 changes made)

{com}. lab values trust_localcharities trust
{txt}
{com}. rename q27o trust_Amer_economy
{res}{txt}
{com}. recode trust_Amer_economy 1=4 2=3 3=2 4=1
{txt}(trust_Amer_economy: 7800 changes made)

{com}. lab values trust_Amer_economy trust
{txt}
{com}. rename q27p trust_banks
{res}{txt}
{com}. recode trust_banks 1=4 2=3 3=2 4=1
{txt}(trust_banks: 7800 changes made)

{com}. lab values trust_banks trust
{txt}
{com}. rename q27q trust_StateUniv
{res}{txt}
{com}. recode trust_StateUniv 1=4 2=3 3=2 4=1
{txt}(trust_StateUniv: 7800 changes made)

{com}. lab values trust_StateUniv trust
{txt}
{com}. rename q29 fedgov_feel
{res}{txt}
{com}. rename q30 msagov_feel
{res}{txt}
{com}. 
. rename q28a change_trust_democraticparty
{res}{txt}
{com}. recode change_trust_democraticparty 1=1 2=3 3=2 8=.
{txt}(change_trust_democraticparty: 1944 changes made)

{com}. lab define trust_change 1 "Trust them more" 2 "About the same" 3 "Trust them less" 
{txt}
{com}. lab values change_trust_democraticparty trust_change
{txt}
{com}. rename q28b change_trust_republicanparty
{res}{txt}
{com}. recode change_trust_republicanparty 1=1 2=3 3=2 8=.
{txt}(change_trust_republicanparty: 1992 changes made)

{com}. lab values change_trust_republicanparty trust_change
{txt}
{com}. rename q28c change_trust_polleaders
{res}{txt}
{com}. recode change_trust_polleaders 1=1 2=3 3=2 8=.
{txt}(change_trust_polleaders: 2157 changes made)

{com}. lab values change_trust_polleaders trust_change
{txt}
{com}. rename q28d change_trust_businessleaders
{res}{txt}
{com}. recode change_trust_businessleaders 1=1 2=3 3=2 8=.
{txt}(change_trust_businessleaders: 2093 changes made)

{com}. lab values change_trust_businessleaders trust_change
{txt}
{com}. rename q28e change_trust_unionleaders
{res}{txt}
{com}. recode change_trust_unionleaders 1=1 2=3 3=2 8=.
{txt}(change_trust_unionleaders: 1979 changes made)

{com}. lab values change_trust_unionleaders trust_change
{txt}
{com}. rename q28f change_trust_companies
{res}{txt}
{com}. recode change_trust_companies 1=1 2=3 3=2 8=.
{txt}(change_trust_companies: 1998 changes made)

{com}. lab values change_trust_companies trust_change
{txt}
{com}. rename q28g change_trust_whites
{res}{txt}
{com}. recode change_trust_whites 1=1 2=3 3=2 8=.
{txt}(change_trust_whites: 1954 changes made)

{com}. lab values change_trust_whites trust_change
{txt}
{com}. rename q28h change_trust_blacks
{res}{txt}
{com}. recode change_trust_blacks 1=1 2=3 3=2 8=.
{txt}(change_trust_blacks: 1884 changes made)

{com}. lab values change_trust_blacks trust_change
{txt}
{com}. rename q28i change_trust_latinos
{res}{txt}
{com}. recode change_trust_latinos 1=1 2=3 3=2 8=.
{txt}(change_trust_latinos: 1923 changes made)

{com}. lab values change_trust_latinos trust_change
{txt}
{com}. rename q28j change_trust_localgovmsa
{res}{txt}
{com}. recode change_trust_localgovmsa 1=1 2=3 3=2 8=.
{txt}(change_trust_localgovmsa: 2010 changes made)

{com}. lab values change_trust_localgovmsa trust_change
{txt}
{com}. rename q28k change_trust_stategov
{res}{txt}
{com}. recode change_trust_stategov 1=1 2=3 3=2 8=.
{txt}(change_trust_stategov: 2048 changes made)

{com}. lab values change_trust_stategov trust_change
{txt}
{com}. rename q28l change_trust_fedgov
{res}{txt}
{com}. recode change_trust_fedgov 1=1 2=3 3=2 8=.
{txt}(change_trust_fedgov: 2165 changes made)

{com}. lab values change_trust_fedgov trust_change
{txt}
{com}. rename q28m change_trust_localschools
{res}{txt}
{com}. recode change_trust_localschools 1=1 2=3 3=2 8=.
{txt}(change_trust_localschools: 1913 changes made)

{com}. lab values change_trust_localschools trust_change
{txt}
{com}. rename q28n change_trust_localcharities
{res}{txt}
{com}. recode change_trust_localcharities 1=1 2=3 3=2 8=.
{txt}(change_trust_localcharities: 1793 changes made)

{com}. lab values change_trust_localcharities trust_change
{txt}
{com}. rename q28o change_trust_Amer_economy
{res}{txt}
{com}. recode change_trust_Amer_economy 1=1 2=3 3=2 8=.
{txt}(change_trust_Amer_economy: 2015 changes made)

{com}. lab values change_trust_Amer_economy trust_change
{txt}
{com}. rename q28p change_trust_banks
{res}{txt}
{com}. recode change_trust_banks 1=1 2=3 3=2 8=.
{txt}(change_trust_banks: 2078 changes made)

{com}. lab values change_trust_banks trust_change
{txt}
{com}. rename q28q change_trust_StateUniv
{res}{txt}
{com}. recode change_trust_StateUniv 1=1 2=3 3=2 8=.
{txt}(change_trust_StateUniv: 1834 changes made)

{com}. lab values change_trust_StateUniv trust_change
{txt}
{com}. 
. foreach x of varlist trust_democraticparty trust_republicanparty trust_polleaders trust_businessleaders ///
> trust_unionleaders trust_companies trust_whites trust_blacks trust_latinos trust_localgovMsa trust_stategov ///
> trust_fedgov trust_localschools trust_localcharities trust_Amer_economy trust_banks trust_StateUniv {c -(}
{txt}  2{com}. g high_`x'=(`x'==3 | `x'==4) if `x'!=.
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach x of varlist trust_democraticparty trust_republicanparty trust_polleaders trust_businessleaders ///
> trust_unionleaders trust_companies trust_whites trust_blacks trust_latinos trust_localgovMsa trust_stategov ///
> trust_fedgov trust_localschools trust_localcharities trust_Amer_economy trust_banks trust_StateUniv {c -(}
{txt}  2{com}. g vhigh_`x'=.
{txt}  3{com}. replace vhigh_`x'=1 if `x'==4
{txt}  4{com}. replace vhigh_`x'=0 if `x'==1
{txt}  5{com}. {c )-}
{txt}(7,800 missing values generated)
(891 real changes made)
(2,274 real changes made)
(7,800 missing values generated)
(627 real changes made)
(3,011 real changes made)
(7,800 missing values generated)
(242 real changes made)
(2,646 real changes made)
(7,800 missing values generated)
(499 real changes made)
(1,007 real changes made)
(7,800 missing values generated)
(644 real changes made)
(1,705 real changes made)
(7,800 missing values generated)
(555 real changes made)
(737 real changes made)
(7,800 missing values generated)
(1,380 real changes made)
(385 real changes made)
(7,800 missing values generated)
(1,500 real changes made)
(481 real changes made)
(7,800 missing values generated)
(1,451 real changes made)
(520 real changes made)
(7,800 missing values generated)
(550 real changes made)
(1,488 real changes made)
(7,800 missing values generated)
(547 real changes made)
(1,552 real changes made)
(7,800 missing values generated)
(345 real changes made)
(3,003 real changes made)
(7,800 missing values generated)
(1,343 real changes made)
(519 real changes made)
(7,800 missing values generated)
(1,751 real changes made)
(451 real changes made)
(7,800 missing values generated)
(775 real changes made)
(1,099 real changes made)
(7,800 missing values generated)
(828 real changes made)
(1,214 real changes made)
(7,800 missing values generated)
(1,540 real changes made)
(519 real changes made)

{com}. 
. rename q58 race
{res}{txt}
{com}. g white=(race==1)
{txt}
{com}. g black=(race==2) 
{txt}
{com}. g latino=(race==3)
{txt}
{com}. g other=(race>3)
{txt}
{com}. g other_than_bw=(race>=3)
{txt}
{com}. g non_white=(race!=1)
{txt}
{com}. 
. g whites_high_trust_blacks=high_trust_blacks if white==1
{txt}(2,138 missing values generated)

{com}. g whites_vhigh_trust_blacks=vhigh_trust_blacks if white==1
{txt}(6,399 missing values generated)

{com}. 
. lab def temp1 0 "Low Trust Amr Econ" 1 "High Trust Amr Econ"
{txt}
{com}. lab val high_trust_Amer_economy temp1
{txt}
{com}. 
. lab def temp2 0 "Very Low Trust Amr Econ" 1 "Very High Trust Amr Econ"
{txt}
{com}. lab val vhigh_trust_Amer_economy temp2
{txt}
{com}. 
. lab def temp3 0 "Low Trust Local Gov" 1 "High Trust Local Gov"
{txt}
{com}. lab val high_trust_localgovMsa temp3
{txt}
{com}. 
. lab def temp4 0 "Very Low Trust Local Gov" 1 "Very High Trust Local Gov"
{txt}
{com}. lab val vhigh_trust_localgovMsa temp4
{txt}
{com}. 
. lab def temp5 0 "Low Trust Schools" 1 "High Trust Schools"
{txt}
{com}. lab val high_trust_localschools temp5
{txt}
{com}. 
. lab def temp6 0 "Very Low Trust Schools" 1 "Very High Trust Schools"
{txt}
{com}. lab val vhigh_trust_localschools temp6
{txt}
{com}. 
. lab def temp7 0 "Low Trust Uni" 1 "High Trust Uni"
{txt}
{com}. lab val high_trust_StateUniv temp7
{txt}
{com}. 
. lab def temp8 0 "Very Low Trust Uni" 1 "Very High Trust Uni"
{txt}
{com}. lab val vhigh_trust_StateUniv temp8
{txt}
{com}. 
. lab def temp9 0 "Low Trust Charities" 1 "High Trust Charities"
{txt}
{com}. lab val high_trust_localcharities temp9
{txt}
{com}. 
. lab def temp10 0 "Very Low Trust Charities" 1 "Very High Trust Charities"
{txt}
{com}. lab val vhigh_trust_localcharities temp10
{txt}
{com}. 
. lab def temp11 0 "Low Trust Blacks" 1 "High Trust Blacks"
{txt}
{com}. lab val whites_high_trust_blacks temp11
{txt}
{com}. 
. lab def temp12 0 "Very Low Trust Blacks" 1 "Very High Trust Blacks"
{txt}
{com}. lab val whites_vhigh_trust_blacks temp12
{txt}
{com}. 
. egen avr_high_trust_inst=rowmean(high_trust_democraticparty high_trust_republicanparty high_trust_polleaders high_trust_businessleaders high_trust_unionleaders ///
> high_trust_companies high_trust_localgovMsa high_trust_stategov high_trust_fedgov high_trust_localschools high_trust_localcharities high_trust_Amer_economy high_trust_banks high_trust_StateUniv)
{txt}
{com}. 
. egen avr_high_trust_inst2=rowmean(high_trust_Amer_economy high_trust_localcharities high_trust_StateUniv high_trust_localschools)
{txt}
{com}. 
. lab def hardwork 1 "Hard work more important" 2 "Equally important" 3 "Luck more important"
{txt}
{com}. lab val hard_work_vs_luck_now hardwork
{txt}
{com}. 
. lab def hardworkthen 1 "Hard work was more important" 2 "Equally important" 3 "Luck was more important"
{txt}
{com}. lab val hard_work_vs_luck_then hardworkthen
{txt}
{com}. 
. * ASC
. * Submission
. rename q31a believe_leaders
{res}{txt}
{com}. rename q31b leaders_know_best
{res}{txt}
{com}. rename q31c criticize_authorities_rev
{res}{txt}
{com}. recode criticize_authorities_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(criticize_authorities_rev: 5232 changes made)

{com}. rename q31d authorities_truthful
{res}{txt}
{com}. rename q31e skeptical_authorities_rev
{res}{txt}
{com}. recode skeptical_authorities_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(skeptical_authorities_rev: 5399 changes made)

{com}. rename q31f questioning_healthy_rev
{res}{txt}
{com}. recode questioning_healthy_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(questioning_healthy_rev: 6348 changes made)

{com}. 
. gen avg_submission = (believe_leaders + leaders_know_best + criticize_authorities_rev + authorities_truthful + skeptical_authorities_rev + questioning_healthy_rev) / 6
{txt}
{com}. alpha believe_leaders leaders_know_best criticize_authorities_rev authorities_truthful skeptical_authorities_rev questioning_healthy_rev, item

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
believe_le~s{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6912{col 45} 0.5087{col 59} .2621337{col 73} 0.6431
{txt}leaders_kn~t{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6647{col 45} 0.4764{col 59} .2731607{col 73} 0.6539
{txt}criticize_~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6021{col 45} 0.3975{col 59} .2971515{col 73} 0.6784
{txt}authoritie~l{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6616{col 45} 0.4725{col 59} .2743767{col 73} 0.6552
{txt}skeptical_~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6296{col 45} 0.4283{col 59}  .286097{col 73} 0.6690
{txt}questionin~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.5696{col 45} 0.3419{col 59} .3082846{col 73} 0.6966
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59}  .283534{col 73} 0.7058
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. * Conventionalism
. rename q31g too_much_tradition_rev
{res}{txt}
{com}. recode too_much_tradition_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(too_much_tradition_rev: 5015 changes made)

{com}. rename q31h traditions_foundation_society
{res}{txt}
{com}. rename q31i follow_social_traditions
{res}{txt}
{com}. rename q31j traditions_interf_progress_rev
{res}{txt}
{com}. recode traditions_interf_progress_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(traditions_interf_progress_rev: 5082 changes made)

{com}. rename q31k challenge_social_traditions_rev
{res}{txt}
{com}. recode challenge_social_traditions_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(challenge_social_traditions_rev: 4850 changes made)

{com}. rename q31l respect_norms
{res}{txt}
{com}. 
. gen avg_conventionalism = (too_much_tradition_rev + traditions_foundation_society + follow_social_traditions + traditions_interf_progress_rev + challenge_social_traditions_rev + respect_norms) / 6
{txt}
{com}. alpha too_much_tradition_rev traditions_foundation_society follow_social_traditions traditions_interf_progress_rev challenge_social_traditions_rev respect_norms, item

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
too_much_t~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.7211{col 45} 0.5498{col 59} .3037454{col 73} 0.6905
{txt}traditions~y{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.7055{col 45} 0.5474{col 59} .3177182{col 73} 0.6928
{txt}follow_soc~s{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.5992{col 45} 0.3987{col 59} .3572446{col 73} 0.7331
{txt}traditions~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.7029{col 45} 0.5272{col 59} .3123952{col 73} 0.6974
{txt}challenge_~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6611{col 45} 0.4760{col 59} .3314619{col 73} 0.7122
{txt}respect_no~s{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.5881{col 45} 0.4050{col 59} .3653477{col 73} 0.7302
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .3313188{col 73} 0.7462
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. * Aggression
. rename q31m force_necessary_groups_threat
{res}{txt}
{com}. rename q31n force_necessary_indv_threat
{res}{txt}
{com}. rename q31o police_avoid_violence_rev
{res}{txt}
{com}. recode police_avoid_violence_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(police_avoid_violence_rev: 5421 changes made)

{com}. rename q31p people_avoid_violence_rev
{res}{txt}
{com}. recode people_avoid_violence_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(people_avoid_violence_rev: 5406 changes made)

{com}. rename q31q force_wrong_rev
{res}{txt}
{com}. recode force_wrong_rev (1=5) (2=4) (3=3) (4=2) (5=1)
{txt}(force_wrong_rev: 5249 changes made)

{com}. rename q31r strong_punishments_necessary
{res}{txt}
{com}. 
. g avg_aggression = (force_necessary_groups_threat + force_necessary_indv_threat + police_avoid_violence_rev + people_avoid_violence_rev + force_wrong_rev + strong_punishments_necessary) / 6
{txt}
{com}. alpha force_necessary_groups_threat force_necessary_indv_threat police_avoid_violence_rev people_avoid_violence_rev force_wrong_rev strong_punishments_necessary, item

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
for~s_threat{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6838{col 45} 0.5178{col 59}  .402948{col 73} 0.7060
{txt}for~v_threat{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6924{col 45} 0.5202{col 59} .3947256{col 73} 0.7049
{txt}police_avo~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6877{col 45} 0.5120{col 59} .3962726{col 73} 0.7072
{txt}people_avo~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.5421{col 45} 0.3421{col 59} .4709564{col 73} 0.7507
{txt}force_wron~v{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6932{col 45} 0.5193{col 59} .3934233{col 73} 0.7051
{txt}strong_pun~y{col 14}{c |}{res}{col 16}7800{col 24}+{col 31} 0.6932{col 45} 0.5167{col 59} .3923753{col 73} 0.7058
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .4084502{col 73} 0.7497
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. gen asc = (avg_aggression + avg_submission + avg_conventionalism) / 3
{txt}
{com}. 
. * Public officials
. rename q32 agree_msa_offic_dont_care
{res}{txt}
{com}. recode agree_msa_offic_dont_care 1=5 2=4 3=3 4=2 5=1
{txt}(agree_msa_offic_dont_care: 5781 changes made)

{com}. *lab define agree 1 "Disagree Strongly" 2 "Disagree Somewhat" 3 "Neither Agree nor Disagree" 4 "Agree Somewhat" 5 "Agree Strongly"
. lab values agree_msa_offic_dont_care agree
{txt}
{com}. rename q33 agree_nat_offic_dont_care
{res}{txt}
{com}. recode agree_nat_offic_dont_care 1=5 2=4 3=3 4=2 5=1
{txt}(agree_nat_offic_dont_care: 6462 changes made)

{com}. lab values agree_nat_offic_dont_care agree
{txt}
{com}. rename q34 agree_dont_have_say_local
{res}{txt}
{com}. recode agree_dont_have_say_local 1=5 2=4 3=3 4=2 5=1
{txt}(agree_dont_have_say_local: 5934 changes made)

{com}. lab values agree_dont_have_say_local agree
{txt}
{com}. rename q35 agree_dont_have_say_nat
{res}{txt}
{com}. recode agree_dont_have_say_nat 1=5 2=4 3=3 4=2 5=1
{txt}(agree_dont_have_say_nat: 6428 changes made)

{com}. lab values agree_dont_have_say_nat agree
{txt}
{com}. 
. * Anxiety
. rename q36a nervous_anxious
{res}{txt}
{com}. rename q36b not_stop_worrying
{res}{txt}
{com}. rename q36c worry_too_much
{res}{txt}
{com}. rename q36d trouble_relaxing
{res}{txt}
{com}. rename q36e hard_sit_still
{res}{txt}
{com}. rename q36f easily_annoyed
{res}{txt}
{com}. rename q36g awful_might_happen
{res}{txt}
{com}. rename q37 anxiety_impact
{res}{txt}
{com}. 
. * Racial resentment
. g racial_resent_1=raciala
{txt}
{com}. recode racial_resent_1 1=5 2=4 3=3 4=2 5=1
{txt}(racial_resent_1: 5823 changes made)

{com}. g racial_resent_2=racialb
{txt}
{com}. recode racial_resent_2 1=5 2=4 3=3 4=2 5=1
{txt}(racial_resent_2: 5823 changes made)

{com}. g racial_resent_3=racialc
{txt}
{com}. g racial_resent_4=raciald
{txt}
{com}. g racial_resent=(racial_resent_1 + racial_resent_2 + racial_resent_3 + racial_resent_4)/4
{txt}
{com}. 
. g racial_tolerance_1=raciala
{txt}
{com}. g racial_tolerance_2=racialb
{txt}
{com}. g racial_tolerance_3=racialc
{txt}
{com}. recode racial_tolerance_3 1=5 2=4 3=3 4=2 5=1
{txt}(racial_tolerance_3: 5533 changes made)

{com}. g racial_tolerance_4=raciald
{txt}
{com}. recode racial_tolerance_4 1=5 2=4 3=3 4=2 5=1
{txt}(racial_tolerance_4: 6388 changes made)

{com}. g racial_tolerance=(racial_tolerance_1 + racial_tolerance_2 + racial_tolerance_3 + racial_tolerance_4)/4
{txt}
{com}. 
. * Demograhics
. rename q38_1 divorce
{res}{txt}
{com}. recode divorce 2=0
{txt}(divorce: 6884 changes made)

{com}. lab def dummy 1 "Yes" 0 "No"
{txt}
{com}. lab val divorce dummy 
{txt}
{com}. 
. rename q38_2 unemployment
{res}{txt}
{com}. recode unemployment 2=0
{txt}(unemployment: 5901 changes made)

{com}. lab val unemployment dummy 
{txt}
{com}. 
. rename q38_3 death
{res}{txt}
{com}. recode death 2=0
{txt}(death: 5819 changes made)

{com}. lab val death dummy 
{txt}
{com}. 
. rename q38_4 alc_drugs
{res}{txt}
{com}. recode alc_drugs 2=0
{txt}(alc_drugs: 6391 changes made)

{com}. lab val alc_drugs dummy 
{txt}
{com}. 
. rename q38_5 hospitalized
{res}{txt}
{com}. recode hospitalized 2=0
{txt}(hospitalized: 6320 changes made)

{com}. lab val hospitalized dummy 
{txt}
{com}. 
. rename q38_6 illnes
{res}{txt}
{com}. recode illnes 2=0
{txt}(illnes: 6650 changes made)

{com}. lab val illnes dummy 
{txt}
{com}. 
. rename q38_7 none
{res}{txt}
{com}. recode none 2=0
{txt}(none: 4613 changes made)

{com}. lab val none dummy 
{txt}
{com}. 
. rename q38a people_rely_on
{res}{txt}
{com}. 
. rename q39 other_groups_favored
{res}{txt}
{com}. recode other_groups_favored 1=5 2=4 3=3 4=2 5=1
{txt}(other_groups_favored: 5432 changes made)

{com}. lab values other_groups_favored agree
{txt}
{com}. 
. rename q40 conservatism
{res}{txt}
{com}. recode conservatism 1=5 2=4 3=3 4=2 5=1
{txt}(conservatism: 5003 changes made)

{com}. lab define cons 1 "Very liberal" 2 "Moderately liberal" 3 "Middle-of-the-road" 4 "Moderately conservative" 5 "Very conservative"
{txt}
{com}. lab values conservatism cons
{txt}
{com}. 
. rename q41 party_id
{res}{txt}
{com}. recode party_id 4=.
{txt}(party_id: 469 changes made)

{com}. gen party_strong_leaning=1 if party_id==1 & q41_a==1
{txt}(6,182 missing values generated)

{com}. replace party_strong_leaning=2 if party_id==1 & q41_a==2
{txt}(1,123 real changes made)

{com}. replace party_strong_leaning=3 if party_id==3 & q41_b==2
{txt}(763 real changes made)

{com}. replace party_strong_leaning=4 if party_id==3 & q41_b==3
{txt}(1,186 real changes made)

{com}. replace party_strong_leaning=5 if party_id==3 & q41_b==1
{txt}(604 real changes made)

{com}. replace party_strong_leaning=6 if party_id==2 & q41_a==2
{txt}(916 real changes made)

{com}. replace party_strong_leaning=7 if party_id==2 & q41_a==1
{txt}(1,121 real changes made)

{com}. 
. g democrat=(party_id==1) if party_id!=.
{txt}(469 missing values generated)

{com}. g republican=(party_id==2) if party_id!=.
{txt}(469 missing values generated)

{com}. g independent=(party_id==3) if party_id!=.
{txt}(469 missing values generated)

{com}. 
. g strong_democrat=(party_id==1) if q41_a==1
{txt}(5,061 missing values generated)

{com}. g strong_republican=(party_id==2) if q41_a==1
{txt}(5,061 missing values generated)

{com}. 
. g leaning_democrat=.
{txt}(7,800 missing values generated)

{com}. replace leaning_democrat=1 if (party_id==1 | q41_b==2)
{txt}(3,579 real changes made)

{com}. g leaning_republican=.
{txt}(7,800 missing values generated)

{com}. replace leaning_republican=1 if (party_id==2 | q41_b==1)
{txt}(2,743 real changes made)

{com}. replace leaning_democrat=0 if leaning_republican==1
{txt}(2,743 real changes made)

{com}. replace leaning_republican=0 if leaning_democrat==1
{txt}(3,579 real changes made)

{com}. 
. rename q42 turnout
{res}{txt}
{com}. g did_vote=(turnout==2)
{txt}
{com}. rename q43 vote
{res}{txt}
{com}. g clinton=(vote==1) if vote!=.
{txt}(1,775 missing values generated)

{com}. g trump=(vote==2) if vote!=.
{txt}(1,775 missing values generated)

{com}. g other_candidate=(vote==3) if vote!=.
{txt}(1,775 missing values generated)

{com}. rename q43_other vote_other_txt
{res}{txt}
{com}. 
. rename q44 county
{res}{txt}
{com}. rename q45 zipcode
{res}{txt}
{com}. rename inputstate state
{res}{txt}
{com}. 
. rename q47 birthyear
{res}{txt}
{com}. g age=2017-birthyear
{txt}
{com}. 
. g years_in_msa=age if q46==1
{txt}(4,265 missing values generated)

{com}. replace years_in_msa=1 if q46==2
{txt}(398 real changes made)

{com}. replace years_in_msa=q46_other if q46==3
{txt}(3,867 real changes made)

{com}. 
. gen agelt30=0 if age!=.
{txt}
{com}. replace agelt30=1 if age<=30 & age!=.
{txt}(1,681 real changes made)

{com}. gen age3150=0 if age!=.
{txt}
{com}. replace age3150=1 if age>30 & age<=50 & age!=.
{txt}(2,711 real changes made)

{com}. gen age5165=0 if age!=.
{txt}
{com}. replace age5165=1 if age>50 & age<=65 & age!=.
{txt}(2,161 real changes made)

{com}. gen agegt65=0 if age!=.
{txt}
{com}. replace agegt65=1 if age>65 & age!=.
{txt}(1,247 real changes made)

{com}. 
. rename q48 placeofbirth
{res}{txt}
{com}. 
. g us_citizen=(q49==1)
{txt}
{com}. 
. rename q50 gender
{res}{txt}
{com}. g female=1 if gender==2
{txt}(3,211 missing values generated)

{com}. replace female=0 if gender==1
{txt}(3,201 real changes made)

{com}. lab def gender 0 "Male" 1 "Female"
{txt}
{com}. lab val female gender
{txt}
{com}. 
. rename q51 educ
{res}{txt}
{com}. g university_educ=(educ>=7 & educ<=10)
{txt}
{com}. g above_hs_educ=(educ>=5 & educ<=6)
{txt}
{com}. g college=(educ>=6)
{txt}
{com}. g somecollege=(educ>=5)
{txt}
{com}. 
. rename q52 marital_status
{res}{txt}
{com}. g couple=(marital_status==1 | marital_status==2 | marital_status==7)
{txt}
{com}. 
. g children=(q53==1)
{txt}
{com}. lab def children 0 "No children" 1 "Children"
{txt}
{com}. lab val children children
{txt}
{com}. 
. rename q54 numberofchildren
{res}{txt}
{com}. 
. forval i=1/8 {c -(}
{txt}  2{com}. rename q55_`i'_age child`i'_age 
{txt}  3{com}. rename q55_`i'_gender child`i'_gender
{txt}  4{com}. {c )-}
{res}{txt}
{com}. 
. g single=(marital_status==3 | marital_status==4 | marital_status==5 | marital_status==6)
{txt}
{com}. lab def single 0 "Couple" 1 "Single"
{txt}
{com}. lab val single single
{txt}
{com}. 
. rename q56 own_where_living  
{res}{txt}
{com}. 
. rename q57 home_value_decreased
{res}{txt}
{com}. 
. rename q59 empl_status
{res}{txt}
{com}. g employed=(empl_status>=1 & empl_status<=3)
{txt}
{com}. g laborforce=(empl_status==1 | empl_status==2 | empl_status==3 | empl_status==4)
{txt}
{com}. 
. rename q60 years_workforce
{res}{txt}
{com}. 
. g years_not_wf=age-25-years_workforce
{txt}(874 missing values generated)

{com}. lab var years_not_wf "Years not in the workforce"
{txt}
{com}. 
. g not_wf_history=(years_not_wf>0) & years_not_wf!=.
{txt}
{com}. lab var not_wf_history "History of not in workforce"
{txt}
{com}. lab def wf 0 "Always in workforce" 1 "Sometimes not in workforce"
{txt}
{com}. lab val not_wf_history wf
{txt}
{com}. 
. rename q61 union_member
{res}{txt}
{com}. 
. rename q62 income
{res}{txt}
{com}. 
. rename q63 major_issues_msa_text
{res}{txt}
{com}. 
. rename q64 things_to_be_done_text
{res}{txt}
{com}. 
. rename q65 reasons_overlooked_text
{res}{txt}
{com}. 
. * Survey
. g length=endtime-starttime
{txt}
{com}. 
. rename q66 survey_rating
{res}{txt}
{com}. rename q67 survey_comments_open
{res}{txt}
{com}. 
. gen freqweight=round(weight*10)
{txt}
{com}. 
. * Labels for new variables
. lab var avg_submission "Average submission" 
{txt}
{com}. lab var avg_conventionalism "Average conventionalism"
{txt}
{com}. lab var avg_aggression "Average agression"
{txt}
{com}. lab var asc "Average ASC"
{txt}
{com}. lab var reduce_trade "Trade should be reduced or kept at current level"
{txt}
{com}. lab var did_vote "Voted at last election"
{txt}
{com}. lab var clinton "Voted for Hillary Clinton at last election"
{txt}
{com}. lab var trump "Voted for Donald Trump at last election"
{txt}
{com}. lab var other_candidate "Voted for other candidate at last election"
{txt}
{com}. lab var county "County id"
{txt}
{com}. lab var age "Age"
{txt}
{com}. lab var female "Female"
{txt}
{com}. lab var university_educ "University degree" 
{txt}
{com}. lab var above_hs_educ "Some college or more education"
{txt}
{com}. lab var college "College degree"
{txt}
{com}. lab var somecollege "Some College"
{txt}
{com}. lab var couple "Couple, married or domestic partnership"
{txt}
{com}. lab var employed "In employment"
{txt}
{com}. lab var laborforce "In the laborforce"
{txt}
{com}. lab var attentioncheck "Attention Check Passed=1"
{txt}
{com}. lab var democrat "Identifies as a Democrat"
{txt}
{com}. lab var republican "Identifies as a Republican"
{txt}
{com}. lab var strong_democrat "Strong Democrat"
{txt}
{com}. lab var strong_republican "Strong Republican"
{txt}
{com}. lab var leaning_democrat "Leaning Democrat"
{txt}
{com}. lab var leaning_republican "Leaning Republican"
{txt}
{com}. lab var years_in_msa "Number of years lived in msa"
{txt}
{com}. lab var white "Respondent identifies as white"
{txt}
{com}. lab var black "Respondent identifies as black"
{txt}
{com}. lab var latino "Respondent identifies as latino"
{txt}
{com}. lab var other "Respondent identifies as other"
{txt}
{com}. lab var racial_resent "Racial resentment"
{txt}
{com}. lab var racial_tolerance "Racial tolerance"
{txt}
{com}. lab var party_strong_leaning "Scale of how strong a republican" 
{txt}
{com}. lab var us_citizen "US citizen"
{txt}
{com}. lab var children "Do have children"
{txt}
{com}. lab var most_important_factor_msa "Most Important Factor MSA Performance"
{txt}
{com}. lab var avr_high_trust_inst "Average High Trust in Institutions"
{txt}
{com}. lab var avr_high_trust_inst "Average Low Trust in Institutions v2"
{txt}
{com}. lab var better_off_than_parents "Better off than parents"
{txt}
{com}. lab var children_better_pros "Children have better prospects"
{txt}
{com}. lab var hard_work_vs_luck_now "Hard work or luck more important now"
{txt}
{com}. lab var hard_work_vs_luck_then "Hard work or luck more important then"
{txt}
{com}. lab var other_groups_favored "Other racial groups are favored"
{txt}
{com}. 
. g high_growth=(msa==1 | msa==3 | msa==4 | msa==8)
{txt}
{com}. lab def highlow 0 "Low-Growth MSAs" 1 "High-Growth MSAs"
{txt}
{com}. lab val high_growth highlow
{txt}
{com}. 
. g high_trust_people=(trust_people==2 | trust_people==3) if trust_people!=.
{txt}
{com}. lab var high_trust_people "High Trust in People"
{txt}
{com}. lab def trustpeople 0 "Low Trust People" 1 "High Trust People"
{txt}
{com}. lab val high_trust_people trustpeople
{txt}
{com}. 
. * Groups defined by distribution of outcome
. pctile temp=asc, n(2)
{txt}
{com}. egen med_asc=total(temp)
{txt}
{com}. drop temp
{txt}
{com}. pctile temp=racial_tolerance, n(2)
{txt}
{com}. egen med_racial_tolerance=total(temp)
{txt}
{com}. drop temp
{txt}
{com}. pctile temp=avr_high_trust_inst, n(2)
{txt}
{com}. egen med_high_trust_inst=total(temp)
{txt}
{com}. drop temp
{txt}
{com}. pctile temp=income, n(2)
{txt}
{com}. egen med_income=total(temp)
{txt}
{com}. drop temp
{txt}
{com}. 
. g above_med_asc=(asc>=med_asc) & asc!=.
{txt}
{com}. g above_med_racial_tolerance=(racial_tolerance>=med_racial_tolerance) & racial_tolerance!=.
{txt}
{com}. g above_med_high_trust_inst=(avr_high_trust_inst>=med_high_trust_inst) & avr_high_trust_inst!=.
{txt}
{com}. g above_med_income=(income>=med_income) & income!=.
{txt}
{com}. 
. * Tercile 1 vs 3
. xtile terc_high_trust_inst=avr_high_trust_inst, nq(3)
{txt}
{com}. g terc13_high_trust_inst=.
{txt}(7,800 missing values generated)

{com}. replace terc13_high_trust_inst=1 if terc_high_trust_inst==3
{txt}(1,980 real changes made)

{com}. replace terc13_high_trust_inst=0 if terc_high_trust_inst==1 
{txt}(2,979 real changes made)

{com}. 
. xtile terc_income=income, nq(3)
{txt}
{com}. g terc13_income=.
{txt}(7,800 missing values generated)

{com}. replace terc13_income=1 if terc_income==3
{txt}(2,242 real changes made)

{com}. replace terc13_income=0 if terc_income==1
{txt}(2,766 real changes made)

{com}. 
. * Quartile 1 vs 4
. xtile quar_high_trust_inst=avr_high_trust_inst, nq(4)
{txt}
{com}. g quar14_high_trust_inst=.
{txt}(7,800 missing values generated)

{com}. replace quar14_high_trust_inst=1 if quar_high_trust_inst==4
{txt}(1,487 real changes made)

{com}. replace quar14_high_trust_inst=0 if quar_high_trust_inst==1
{txt}(2,284 real changes made)

{com}. 
. * Value labels
. lab def med_asc 0 "Below-Median ASC" 1 "Above-Median ASC"
{txt}
{com}. lab val above_med_asc med_asc
{txt}
{com}. 
. lab def med_racial 0 "Below-Median Racial Tolerance" 1 "Above-Median Racial Tolerance"
{txt}
{com}. lab val above_med_racial_tolerance med_racial
{txt}
{com}. 
. lab def med_trustinst 0 "Below-Median High Trust Institutions" 1 "Above-Median High Trust Institutions"
{txt}
{com}. lab val above_med_high_trust_inst med_trustinst
{txt}
{com}. 
. lab def med_income 0 "Below-Median Income" 1 "Above-Median Income"
{txt}
{com}. lab val above_med_income med_income
{txt}
{com}. 
. lab def terc13 0 "1st Terc Trust Inst" 1 "3rd Terc Trust Inst"
{txt}
{com}. lab val terc13_high_trust_inst terc13
{txt}
{com}. 
. lab def terc13Income 0 "1st Terc Income" 1 "3rd Terc Income"
{txt}
{com}. lab val terc13_income terc13Income
{txt}
{com}. 
. lab def quar14 0 "1st Quar Trust Inst" 1 "4rd Quar Trust Inst"
{txt}
{com}. lab val quar14_high_trust_inst quar14
{txt}
{com}. 
. lab def college 0 "Less than College" 1 "College or More"
{txt}
{com}. lab val college college
{txt}
{com}. 
. * Shorter labels for graph-file names (cannot be too long)
. g above_med_asc_copy=above_med_asc
{txt}
{com}. g above_med_racial_tolerance_copy=above_med_racial_tolerance
{txt}
{com}. g above_med_high_trust_inst_copy=above_med_high_trust_inst
{txt}
{com}. 
. lab def med_asc2 0 "Low ASC" 1 "High ASC"
{txt}
{com}. lab val above_med_asc_copy med_asc2
{txt}
{com}. 
. lab def med_tolerance2 0 "Low Racial Tolerance" 1 "High Racial Tolerance"
{txt}
{com}. lab val above_med_racial_tolerance_copy med_racial2
{txt}
{com}. 
. lab def med_trustinst2 0 "Low High Trust Inst" 1 "High High Trust Inst"
{txt}
{com}. lab val above_med_high_trust_inst_copy med_trustinst2
{txt}
{com}. 
. 
. 
. *** Save analysis data
. save msa_survey_indiv, replace
{txt}file msa_survey_indiv.dta saved

{com}. 
. 
. 
. 
. 
. 
. ****** Summary statistics by MSA ******
.  
. foreach i of numlist 1/8 {c -(}
{txt}  2{com}. 
. use msa_survey_indiv, clear
{txt}  3{com}. 
. keep if msa==`i'
{txt}  4{com}. tab msa
{txt}  5{com}. 
. local forlab: value label msa
{txt}  6{com}. local label: label `forlab' `i'
{txt}  7{com}. di "`label'"
{txt}  8{com}. 
. *log using "$output/sumstat_`label'.smcl", replace
. 
. tabstat age female white black latino college somecollege laborforce democrat republican clinton trump, s(mean n) c(s)
{txt}  9{com}. tabstat age female white black latino college somecollege laborforce democrat republican clinton trump [fweight=freqweight], s(mean n) c(s)
{txt} 10{com}. 
. *log close
. {c )-}
{txt}( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   Charlotte {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Charlotte

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}   45.662      1000
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5881764       998
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}     .714      1000
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}     .182      1000
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}     .032      1000
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}     .545      1000
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .783      1000
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}     .658      1000
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3615222       946
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res}  .307611       946
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4742138       795
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res}  .445283       795
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res} 45.24753     10027
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5317896     10019
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .6487484     10027
{txt}{ralign 12:black} {...}
{c |}{...}
 {res} .2283834     10027
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res} .0686147     10027
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .4112895     10027
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}  .636282     10027
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}  .636282     10027
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3621425      9615
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .3157566      9615
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res}  .468698      7811
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4967354      7811
{txt}{hline 13}{c BT}{hline 20}
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   Cleveland {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Cleveland

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}   48.642      1000
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5937813       997
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}     .791      1000
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}     .147      1000
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}     .023      1000
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}     .513      1000
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .723      1000
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}     .605      1000
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .4099576       944
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res}      .25       944
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .5149254       804
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .3955224       804
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}  48.9848      9997
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5256667      9974
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .7293188      9997
{txt}{ralign 12:black} {...}
{c |}{...}
 {res} .1851555      9997
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}  .046514      9997
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .3920176      9997
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res} .6118836      9997
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res} .5673702      9997
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .4248352      9559
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .2429124      9559
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .5613567      7872
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4012957      7872
{txt}{hline 13}{c BT}{hline 20}
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Houston {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Houston

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}   44.658      1000
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5505506       999
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}     .509      1000
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}     .167      1000
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}     .233      1000
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}     .475      1000
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .696      1000
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}     .653      1000
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3432519       941
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .2911796       941
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4721448       718
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4387187       718
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res} 44.50528     10034
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5143569     10030
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .4062189     10034
{txt}{ralign 12:black} {...}
{c |}{...}
 {res} .1658362     10034
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res} .3302771     10034
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .3989436     10034
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res} .6013554     10034
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res} .6636436     10034
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3581523      9482
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .2804261      9482
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4884315      7045
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4682754      7045
{txt}{hline 13}{c BT}{hline 20}
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Indianapolis {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Indianapolis

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}   46.502      1000
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .6176176       999
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}     .822      1000
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}     .118      1000
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}     .018      1000
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}     .535      1000
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .763      1000
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}     .632      1000
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3201708       937
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .3436499       937
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4519481       770
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res}  .438961       770
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res} 46.52927     10010
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5230954     10002
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .7618382     10010
{txt}{ralign 12:black} {...}
{c |}{...}
 {res} .1408591     10010
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}  .048951     10010
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .4247752     10010
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res} .6234765     10010
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res} .6250749     10010
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3146743      9486
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .3572633      9486
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4386412      7448
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .5115467      7448
{txt}{hline 13}{c BT}{hline 20}
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Memphis {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Memphis

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}   45.653      1000
{txt}{ralign 12:female} {...}
{c |}{...}
 {res}     .629      1000
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}     .614      1000
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}     .326      1000
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}     .015      1000
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}     .506      1000
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .798      1000
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}     .652      1000
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res}  .373262       935
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .3122995       935
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4722222       720
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4458333       720
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res} 45.03348     10035
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5327354     10035
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .4759342     10035
{txt}{ralign 12:black} {...}
{c |}{...}
 {res} .4522172     10035
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res} .0344793     10035
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .3569507     10035
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res} .6001993     10035
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res} .6314898     10035
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .4223812      9508
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .2709297      9508
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .5509702      6906
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4184767      6906
{txt}{hline 13}{c BT}{hline 20}
( )
(7,000 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   Rochester {c |}{res}        800      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        800      100.00
Rochester

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}  48.4425       800
{txt}{ralign 12:female} {...}
{c |}{...}
 {res}     .615       800
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}    .8375       800
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}   .06375       800
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}   .04375       800
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}    .5825       800
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .755       800
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}      .59       800
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3574297       747
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res}  .291834       747
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4812398       613
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4241436       613
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}  48.1245      7992
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5267768      7992
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .8080581      7992
{txt}{ralign 12:black} {...}
{c |}{...}
 {res} .0924675      7992
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res} .0566817      7992
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .4732232      7992
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res} .6411411      7992
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res} .5741992      7992
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3363079      7508
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .2870272      7508
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4889152      5909
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4567609      5909
{txt}{hline 13}{c BT}{hline 20}
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   St. Louis {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
St. Louis

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}   48.127      1000
{txt}{ralign 12:female} {...}
{c |}{...}
 {res}     .575      1000
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}     .809      1000
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}     .131      1000
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}     .013      1000
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}     .533      1000
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .768      1000
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}     .619      1000
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3822598       947
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .2555438       947
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res}  .499385       813
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .3911439       813
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res} 48.25352     10011
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5176306     10011
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .7715513     10011
{txt}{ralign 12:black} {...}
{c |}{...}
 {res} .1592249     10011
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res} .0271701     10011
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .4196384     10011
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res} .6329038     10011
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res} .6012386     10011
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .3719086      9583
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .2797663      9583
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .4756634      7951
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .4770469      7951
{txt}{hline 13}{c BT}{hline 20}
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Seattle {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Seattle

{txt}{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res}   45.995      1000
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5486459       997
{txt}{ralign 12:white} {...}
{c |}{...}
 {res}     .733      1000
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}     .037      1000
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res}     .051      1000
{txt}{ralign 12:college} {...}
{c |}{...}
 {res}     .577      1000
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res}     .782      1000
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res}     .642      1000
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .4400428       934
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .1734475       934
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .6111111       792
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .2651515       792
{txt}{hline 13}{c BT}{hline 20}

{ralign 12:variable} {...}
{c |}      mean         N
{hline 13}{c +}{hline 20}
{ralign 12:age} {...}
{c |}{...}
 {res} 46.14689     10021
{txt}{ralign 12:female} {...}
{c |}{...}
 {res} .5054032      9994
{txt}{ralign 12:white} {...}
{c |}{...}
 {res} .6857599     10021
{txt}{ralign 12:black} {...}
{c |}{...}
 {res}  .054286     10021
{txt}{ralign 12:latino} {...}
{c |}{...}
 {res} .0753418     10021
{txt}{ralign 12:college} {...}
{c |}{...}
 {res} .5167149     10021
{txt}{ralign 12:somecollege} {...}
{c |}{...}
 {res} .7164954     10021
{txt}{ralign 12:laborforce} {...}
{c |}{...}
 {res} .6322722     10021
{txt}{ralign 12:democrat} {...}
{c |}{...}
 {res} .4363521      9474
{txt}{ralign 12:republican} {...}
{c |}{...}
 {res} .1881993      9474
{txt}{ralign 12:clinton} {...}
{c |}{...}
 {res} .6313812      7718
{txt}{ralign 12:trump} {...}
{c |}{...}
 {res} .2973568      7718
{txt}{hline 13}{c BT}{hline 20}

{com}. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. 
. 
. 
. *** Code to run the conjoint analyses (Figure 2, 3, A-4, and Table A-5, A-6)
. do "$localdir/Code/conjoint_analysis.do"
{txt}
{com}. /*
> This program generates the conjoint data and produces conjoint 
> results shown in Figure 2, Figure 3, Figure A-4, and Table A-5 and A-6 of 
> the paper and online appendix 
> */
. 
. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. set matsize 800
{txt}
{com}. set memory 200m
{txt}{bf:set memory} ignored.
{p 4 4 2}
Memory no longer
needs to be set in modern Statas;
memory adjustments are performed on the fly
automatically.
{p_end}

{com}. 
. cd "$localdir/Data"
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Data
{txt}
{com}. gl output "$localdir/Output"
{txt}
{com}. 
. 
. 
. ***** Conjoint on pooled sample - All MSAs together *****
. 
. *** Load analysis data
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. 
. *** Generate conjoint data 
. 
. keep caseid q16*
{txt}
{com}. 
. * Number of dimensions: 6
. * Number of comparisons: 5
. * Number of plans per comparison: 2
. * Final conjoint data: 10 rows per respondent, with infomation about 6 dimensions and whether the plan was chosen
. 
. * Make 5 comparisions into rows per policy dimension
. foreach policy in educ hieduc invest gov workers local {c -(}
{txt}  2{com}. rename q16a_1_`policy' `policy'11
{txt}  3{com}. rename q16b_1_`policy' `policy'12
{txt}  4{com}. rename q16c_1_`policy' `policy'13
{txt}  5{com}. rename q16d_1_`policy' `policy'14
{txt}  6{com}. rename q16e_1_`policy' `policy'15
{txt}  7{com}. rename q16a_2_`policy' `policy'21
{txt}  8{com}. rename q16b_2_`policy' `policy'22
{txt}  9{com}. rename q16c_2_`policy' `policy'23
{txt} 10{com}. rename q16d_2_`policy' `policy'24
{txt} 11{com}. rename q16e_2_`policy' `policy'25
{txt} 12{com}. {c )-}
{res}{txt}
{com}. 
. reshape long educ1 hieduc1 invest1 gov1 workers1 local1 educ2 hieduc2 invest2 gov2 workers2 local2, i(caseid) j(table)
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    7800   {txt}->{res}   39000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}

{com}. 
. * Make 2 rows for each 5 comparisons, for each 6 policy dimensions
. sort caseid table
{txt}
{com}. g count=_n
{txt}
{com}. 
. reshape long educ hieduc invest gov workers local, i(count) j(plan)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}   39000   {txt}->{res}   78000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}

{com}. drop count
{txt}
{com}. 
. rename q16_a table1_choice
{res}{txt}
{com}. rename q16_b table2_choice
{res}{txt}
{com}. rename q16_c table3_choice
{res}{txt}
{com}. rename q16_d table4_choice
{res}{txt}
{com}. rename q16_e table5_choice
{res}{txt}
{com}. 
. g plan_chosen=.
{txt}(78,000 missing values generated)

{com}. forval t=1/5 {c -(}
{txt}  2{com}. replace plan_chosen=1 if table==`t' & plan==1 & table`t'_choice==1 
{txt}  3{com}. replace plan_chosen=0 if table==`t' & plan==1 & table`t'_choice==2
{txt}  4{com}. replace plan_chosen=1 if table==`t' & plan==2 & table`t'_choice==2 
{txt}  5{com}. replace plan_chosen=0 if table==`t' & plan==2 & table`t'_choice==1 
{txt}  6{com}. {c )-}
{txt}(4,119 real changes made)
(3,681 real changes made)
(3,681 real changes made)
(4,119 real changes made)
(4,024 real changes made)
(3,776 real changes made)
(3,776 real changes made)
(4,024 real changes made)
(4,043 real changes made)
(3,757 real changes made)
(3,757 real changes made)
(4,043 real changes made)
(4,090 real changes made)
(3,710 real changes made)
(3,710 real changes made)
(4,090 real changes made)
(4,071 real changes made)
(3,729 real changes made)
(3,729 real changes made)
(4,071 real changes made)

{com}. 
. drop table1_choice- table5_choice
{txt}
{com}. 
. 
. *** Merge with other information from the survey
. 
. merge m:1 caseid using msa_survey_indiv
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}          78,000{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. 
. save "all_conjoint.dta", replace
{txt}file all_conjoint.dta saved

{com}. 
. 
. *** Graphs and tables
. use "all_conjoint.dta", clear
{txt}( )

{com}. set scheme s1mono // Graph layout
{txt}
{com}. 
. * Short value labels
. lab def educ 1 "Charter schools" 2 "Vouchers to schools" 3 "Free pre-school" 4 "Pay teachers more" 5 "Keep current" 
{txt}
{com}. lab def hieduc 1 "Community colleges" 2 "Local public universities" 3 "Technical vocational training" 4 "Student grant programs" 5 "Keep current" 
{txt}
{com}. lab def invest 1 "Attract new businesses" 2 "Stimulate existing companies" 3 "Encourage investment charities" 4 "Keep current" 
{txt}
{com}. lab def gov 1 "Consolidate local government" 2 "More power to the state" 3 "Keep current"
{txt}
{com}. lab def workers 1 "Limit unions' power" 2 "Expand unions' power" 3 "Worker training vouchers" 4 "Tax breaks to entrepreneurs" 5 "Keep current"
{txt}
{com}. lab def local 1 "Affordable housing" 2 "Public transportation" 3 "Safety and crime prevention" 4 "Keep current"   
{txt}
{com}. lab values educ educ
{txt}
{com}. lab values hieduc hieduc
{txt}
{com}. lab values invest invest
{txt}
{com}. lab values gov gov
{txt}
{com}. lab values workers workers
{txt}
{com}. lab values local local
{txt}
{com}. 
. * Figure and table with pooled conjoint results
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight], cluster(caseid)
{txt}(sum of wgt is 78,000)

Linear regression                               Number of obs     = {res}    78,000
                                                {txt}F(20, 7799)       =  {res}    26.88
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0115
                                                {txt}Root MSE          =    {res} .49719

{txt}{ralign 97:(Std. Err. adjusted for {res:7,800} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0200418{col 45}{space 2} .0073682{col 56}{space 1}   -2.72{col 65}{space 3}0.007{col 73}{space 4}-.0344855{col 86}{space 3}-.0055981
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0083856{col 45}{space 2} .0075842{col 56}{space 1}    1.11{col 65}{space 3}0.269{col 73}{space 4}-.0064815{col 86}{space 3} .0232526
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0206947{col 45}{space 2} .0071244{col 56}{space 1}    2.90{col 65}{space 3}0.004{col 73}{space 4}  .006729{col 86}{space 3} .0346604
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0532324{col 45}{space 2} .0074581{col 56}{space 1}    7.14{col 65}{space 3}0.000{col 73}{space 4} .0386125{col 86}{space 3} .0678524
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0477106{col 45}{space 2} .0072037{col 56}{space 1}    6.62{col 65}{space 3}0.000{col 73}{space 4} .0335895{col 86}{space 3} .0618318
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0238541{col 45}{space 2} .0071991{col 56}{space 1}    3.31{col 65}{space 3}0.001{col 73}{space 4} .0097419{col 86}{space 3} .0379662
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0421149{col 45}{space 2} .0072285{col 56}{space 1}    5.83{col 65}{space 3}0.000{col 73}{space 4} .0279451{col 86}{space 3} .0562847
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2}  .044457{col 45}{space 2} .0072522{col 56}{space 1}    6.13{col 65}{space 3}0.000{col 73}{space 4} .0302408{col 86}{space 3} .0586731
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0528329{col 45}{space 2} .0064904{col 56}{space 1}    8.14{col 65}{space 3}0.000{col 73}{space 4} .0401101{col 86}{space 3} .0655558
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2}  .040298{col 45}{space 2} .0065372{col 56}{space 1}    6.16{col 65}{space 3}0.000{col 73}{space 4} .0274833{col 86}{space 3} .0531126
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0112644{col 45}{space 2} .0068895{col 56}{space 1}    1.64{col 65}{space 3}0.102{col 73}{space 4}-.0022409{col 86}{space 3} .0247698
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0079508{col 45}{space 2} .0057127{col 56}{space 1}    1.39{col 65}{space 3}0.164{col 73}{space 4}-.0032476{col 86}{space 3} .0191492
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2} -.006103{col 45}{space 2} .0057253{col 56}{space 1}   -1.07{col 65}{space 3}0.286{col 73}{space 4}-.0173261{col 86}{space 3} .0051201
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0462484{col 45}{space 2} .0072395{col 56}{space 1}   -6.39{col 65}{space 3}0.000{col 73}{space 4}-.0604396{col 86}{space 3}-.0320571
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0462108{col 45}{space 2} .0072784{col 56}{space 1}   -6.35{col 65}{space 3}0.000{col 73}{space 4}-.0604784{col 86}{space 3}-.0319433
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0105335{col 45}{space 2} .0072133{col 56}{space 1}    1.46{col 65}{space 3}0.144{col 73}{space 4}-.0036065{col 86}{space 3} .0246735
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0261099{col 45}{space 2} .0070816{col 56}{space 1}    3.69{col 65}{space 3}0.000{col 73}{space 4}  .012228{col 86}{space 3} .0399917
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0441466{col 45}{space 2} .0064949{col 56}{space 1}    6.80{col 65}{space 3}0.000{col 73}{space 4} .0314149{col 86}{space 3} .0568783
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0115348{col 45}{space 2} .0065767{col 56}{space 1}    1.75{col 65}{space 3}0.079{col 73}{space 4}-.0013573{col 86}{space 3} .0244268
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0591623{col 45}{space 2} .0065584{col 56}{space 1}    9.02{col 65}{space 3}0.000{col 73}{space 4}  .046306{col 86}{space 3} .0720186
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4115653{col 45}{space 2} .0104223{col 56}{space 1}   39.49{col 65}{space 3}0.000{col 73}{space 4} .3911348{col 86}{space 3} .4319958
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w
{txt}
{com}. local n_baseline = e(N)
{txt}
{com}. *outreg2 using  "$output/tableA5.xls", nocons nor side replace ctitle(Estimate) tex(frag) bdec(3) label addstat(Root MSE, e(rmse),Respondents, e(N_clust))
. 
. coefplot baseline_w, title(/*"All Cities"*/"") mcolor(black) ciopts(lcolor(black) lwidth(thin)) xlabel(-0.15(.05) 0.15) omitted base xline(0) ///
> headings(2.educ = "{c -(}bf:Education{c )-}" 3.hieduc = "{c -(}bf:Higher Education{c )-}" 2.invest = "{c -(}bf:Investment & Taxes{c )-}" 2.gov = "{c -(}bf:Governance{c )-}" ///
> 3.workers = "{c -(}bf:Workers & Entrepreneurs{c )-}" 2.local = "{c -(}bf:Local Services{c )-}") ///
> drop(_cons 5.educ 5.hieduc 4.invest 3.gov 5.workers 4.local) ///
> order(2.invest 3.invest 1.invest 3.workers 4.workers 1.workers 2.workers 2.local 3.local 1.local 2.gov 1.gov 2.educ 4.educ 3.educ 1.educ 3.hieduc 4.hieduc 2.hieduc 1.hieduc) ///
> xscale(titlegap(*6)) yscale(titlegap(*3)) ///
> ylabel(, labsize(medium)) xtitle("Change in Pr(Development Plan Selected)") ytitle("") xsize(5) ysize(7) scale(.6)    
{res}{txt}
{com}. graph export "$output/fig2_a.pdf", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/fig2_a.pdf written in PDF format)

{com}. graph export "$output/fig2_a.eps", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/fig2_a.eps written in EPS format)

{com}. 
. 
. * Table with strong D/R interaction
. g strong_DR = .
{txt}(78,000 missing values generated)

{com}. replace  strong_DR = 0 if strong_dem == 1
{txt}(16,180 real changes made)

{com}. replace  strong_DR = 1 if strong_rep == 1
{txt}(11,210 real changes made)

{com}. lab def strong_DR 0 "Strong Democrat" 1 "Strong Republican" 
{txt}
{com}. lab val strong_DR strong_DR
{txt}
{com}. 
. * Regressions 
. * Everyone together
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight], cluster(caseid)
{txt}(sum of wgt is 78,000)

Linear regression                               Number of obs     = {res}    78,000
                                                {txt}F(20, 7799)       =  {res}    26.88
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0115
                                                {txt}Root MSE          =    {res} .49719

{txt}{ralign 97:(Std. Err. adjusted for {res:7,800} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0200418{col 45}{space 2} .0073682{col 56}{space 1}   -2.72{col 65}{space 3}0.007{col 73}{space 4}-.0344855{col 86}{space 3}-.0055981
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0083856{col 45}{space 2} .0075842{col 56}{space 1}    1.11{col 65}{space 3}0.269{col 73}{space 4}-.0064815{col 86}{space 3} .0232526
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0206947{col 45}{space 2} .0071244{col 56}{space 1}    2.90{col 65}{space 3}0.004{col 73}{space 4}  .006729{col 86}{space 3} .0346604
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0532324{col 45}{space 2} .0074581{col 56}{space 1}    7.14{col 65}{space 3}0.000{col 73}{space 4} .0386125{col 86}{space 3} .0678524
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0477106{col 45}{space 2} .0072037{col 56}{space 1}    6.62{col 65}{space 3}0.000{col 73}{space 4} .0335895{col 86}{space 3} .0618318
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0238541{col 45}{space 2} .0071991{col 56}{space 1}    3.31{col 65}{space 3}0.001{col 73}{space 4} .0097419{col 86}{space 3} .0379662
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0421149{col 45}{space 2} .0072285{col 56}{space 1}    5.83{col 65}{space 3}0.000{col 73}{space 4} .0279451{col 86}{space 3} .0562847
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2}  .044457{col 45}{space 2} .0072522{col 56}{space 1}    6.13{col 65}{space 3}0.000{col 73}{space 4} .0302408{col 86}{space 3} .0586731
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0528329{col 45}{space 2} .0064904{col 56}{space 1}    8.14{col 65}{space 3}0.000{col 73}{space 4} .0401101{col 86}{space 3} .0655558
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2}  .040298{col 45}{space 2} .0065372{col 56}{space 1}    6.16{col 65}{space 3}0.000{col 73}{space 4} .0274833{col 86}{space 3} .0531126
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0112644{col 45}{space 2} .0068895{col 56}{space 1}    1.64{col 65}{space 3}0.102{col 73}{space 4}-.0022409{col 86}{space 3} .0247698
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0079508{col 45}{space 2} .0057127{col 56}{space 1}    1.39{col 65}{space 3}0.164{col 73}{space 4}-.0032476{col 86}{space 3} .0191492
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2} -.006103{col 45}{space 2} .0057253{col 56}{space 1}   -1.07{col 65}{space 3}0.286{col 73}{space 4}-.0173261{col 86}{space 3} .0051201
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0462484{col 45}{space 2} .0072395{col 56}{space 1}   -6.39{col 65}{space 3}0.000{col 73}{space 4}-.0604396{col 86}{space 3}-.0320571
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0462108{col 45}{space 2} .0072784{col 56}{space 1}   -6.35{col 65}{space 3}0.000{col 73}{space 4}-.0604784{col 86}{space 3}-.0319433
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0105335{col 45}{space 2} .0072133{col 56}{space 1}    1.46{col 65}{space 3}0.144{col 73}{space 4}-.0036065{col 86}{space 3} .0246735
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0261099{col 45}{space 2} .0070816{col 56}{space 1}    3.69{col 65}{space 3}0.000{col 73}{space 4}  .012228{col 86}{space 3} .0399917
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0441466{col 45}{space 2} .0064949{col 56}{space 1}    6.80{col 65}{space 3}0.000{col 73}{space 4} .0314149{col 86}{space 3} .0568783
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0115348{col 45}{space 2} .0065767{col 56}{space 1}    1.75{col 65}{space 3}0.079{col 73}{space 4}-.0013573{col 86}{space 3} .0244268
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0591623{col 45}{space 2} .0065584{col 56}{space 1}    9.02{col 65}{space 3}0.000{col 73}{space 4}  .046306{col 86}{space 3} .0720186
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4115653{col 45}{space 2} .0104223{col 56}{space 1}   39.49{col 65}{space 3}0.000{col 73}{space 4} .3911348{col 86}{space 3} .4319958
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Democrats
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local if strong_DR == 0 [pweight=weight], cluster(caseid)
{txt}(sum of wgt is 16,839.2045409739)

Linear regression                               Number of obs     = {res}    16,180
                                                {txt}F(20, 1617)       =  {res}     8.47
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0206
                                                {txt}Root MSE          =    {res} .49514

{txt}{ralign 97:(Std. Err. adjusted for {res:1,618} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0450884{col 45}{space 2} .0161469{col 56}{space 1}   -2.79{col 65}{space 3}0.005{col 73}{space 4}-.0767594{col 86}{space 3}-.0134174
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2}-.0381262{col 45}{space 2} .0166889{col 56}{space 1}   -2.28{col 65}{space 3}0.022{col 73}{space 4}-.0708603{col 86}{space 3}-.0053921
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0524119{col 45}{space 2} .0158746{col 56}{space 1}    3.30{col 65}{space 3}0.001{col 73}{space 4} .0212749{col 86}{space 3}  .083549
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2}  .075387{col 45}{space 2}  .015824{col 56}{space 1}    4.76{col 65}{space 3}0.000{col 73}{space 4} .0443493{col 86}{space 3} .1064248
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0264617{col 45}{space 2} .0165749{col 56}{space 1}    1.60{col 65}{space 3}0.111{col 73}{space 4}-.0060489{col 86}{space 3} .0589723
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0088563{col 45}{space 2} .0157178{col 56}{space 1}    0.56{col 65}{space 3}0.573{col 73}{space 4} -.021973{col 86}{space 3} .0396857
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0400955{col 45}{space 2} .0159223{col 56}{space 1}    2.52{col 65}{space 3}0.012{col 73}{space 4} .0088649{col 86}{space 3}  .071326
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0636447{col 45}{space 2} .0169777{col 56}{space 1}    3.75{col 65}{space 3}0.000{col 73}{space 4}  .030344{col 86}{space 3} .0969454
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0289499{col 45}{space 2} .0135919{col 56}{space 1}    2.13{col 65}{space 3}0.033{col 73}{space 4} .0022904{col 86}{space 3} .0556094
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0221864{col 45}{space 2} .0143539{col 56}{space 1}    1.55{col 65}{space 3}0.122{col 73}{space 4}-.0059677{col 86}{space 3} .0503406
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0133458{col 45}{space 2} .0150599{col 56}{space 1}    0.89{col 65}{space 3}0.376{col 73}{space 4}-.0161932{col 86}{space 3} .0428849
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0162986{col 45}{space 2} .0127923{col 56}{space 1}    1.27{col 65}{space 3}0.203{col 73}{space 4}-.0087926{col 86}{space 3} .0413898
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0051973{col 45}{space 2} .0130324{col 56}{space 1}   -0.40{col 65}{space 3}0.690{col 73}{space 4}-.0307596{col 86}{space 3}  .020365
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0782062{col 45}{space 2} .0166205{col 56}{space 1}   -4.71{col 65}{space 3}0.000{col 73}{space 4}-.1108061{col 86}{space 3}-.0456063
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2} .0103207{col 45}{space 2} .0154802{col 56}{space 1}    0.67{col 65}{space 3}0.505{col 73}{space 4}-.0200427{col 86}{space 3} .0406841
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0227133{col 45}{space 2} .0158564{col 56}{space 1}    1.43{col 65}{space 3}0.152{col 73}{space 4} -.008388{col 86}{space 3} .0538146
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0361211{col 45}{space 2}  .016091{col 56}{space 1}    2.24{col 65}{space 3}0.025{col 73}{space 4} .0045596{col 86}{space 3} .0676825
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0621165{col 45}{space 2}  .014565{col 56}{space 1}    4.26{col 65}{space 3}0.000{col 73}{space 4} .0335481{col 86}{space 3} .0906848
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0195876{col 45}{space 2} .0145619{col 56}{space 1}    1.35{col 65}{space 3}0.179{col 73}{space 4}-.0089746{col 86}{space 3} .0481498
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0147954{col 45}{space 2} .0147964{col 56}{space 1}    1.00{col 65}{space 3}0.317{col 73}{space 4}-.0142267{col 86}{space 3} .0438175
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4204664{col 45}{space 2} .0234681{col 56}{space 1}   17.92{col 65}{space 3}0.000{col 73}{space 4} .3744353{col 86}{space 3} .4664976
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Republicans
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local if strong_DR == 1 [pweight=weight], cluster(caseid)
{txt}(sum of wgt is 11,495.0809689651)

Linear regression                               Number of obs     = {res}    11,210
                                                {txt}F(20, 1120)       =  {res}     8.65
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0229
                                                {txt}Root MSE          =    {res}  .4947

{txt}{ralign 97:(Std. Err. adjusted for {res:1,121} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2} .0456137{col 45}{space 2} .0206727{col 56}{space 1}    2.21{col 65}{space 3}0.028{col 73}{space 4} .0050523{col 86}{space 3} .0861752
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0552234{col 45}{space 2} .0187726{col 56}{space 1}    2.94{col 65}{space 3}0.003{col 73}{space 4}   .01839{col 86}{space 3} .0920568
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} -.019942{col 45}{space 2} .0175166{col 56}{space 1}   -1.14{col 65}{space 3}0.255{col 73}{space 4}-.0543111{col 86}{space 3}  .014427
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0359411{col 45}{space 2} .0175349{col 56}{space 1}    2.05{col 65}{space 3}0.041{col 73}{space 4} .0015361{col 86}{space 3} .0703461
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0349283{col 45}{space 2} .0176058{col 56}{space 1}    1.98{col 65}{space 3}0.048{col 73}{space 4} .0003843{col 86}{space 3} .0694723
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2}-.0019922{col 45}{space 2} .0176894{col 56}{space 1}   -0.11{col 65}{space 3}0.910{col 73}{space 4}-.0367003{col 86}{space 3}  .032716
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0337047{col 45}{space 2} .0183923{col 56}{space 1}    1.83{col 65}{space 3}0.067{col 73}{space 4}-.0023826{col 86}{space 3}  .069792
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0075888{col 45}{space 2} .0179727{col 56}{space 1}    0.42{col 65}{space 3}0.673{col 73}{space 4}-.0276753{col 86}{space 3} .0428528
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0569138{col 45}{space 2} .0153436{col 56}{space 1}    3.71{col 65}{space 3}0.000{col 73}{space 4} .0268083{col 86}{space 3} .0870193
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0563328{col 45}{space 2} .0160848{col 56}{space 1}    3.50{col 65}{space 3}0.000{col 73}{space 4} .0247731{col 86}{space 3} .0878925
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2}-.0023304{col 45}{space 2} .0145926{col 56}{space 1}   -0.16{col 65}{space 3}0.873{col 73}{space 4}-.0309624{col 86}{space 3} .0263016
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0117185{col 45}{space 2}  .014767{col 56}{space 1}    0.79{col 65}{space 3}0.428{col 73}{space 4}-.0172555{col 86}{space 3} .0406926
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2} -.001124{col 45}{space 2} .0140675{col 56}{space 1}   -0.08{col 65}{space 3}0.936{col 73}{space 4}-.0287256{col 86}{space 3} .0264777
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2} -.026115{col 45}{space 2} .0182309{col 56}{space 1}   -1.43{col 65}{space 3}0.152{col 73}{space 4}-.0618855{col 86}{space 3} .0096555
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.1318199{col 45}{space 2} .0184375{col 56}{space 1}   -7.15{col 65}{space 3}0.000{col 73}{space 4}-.1679958{col 86}{space 3}-.0956439
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2}-.0251325{col 45}{space 2} .0180375{col 56}{space 1}   -1.39{col 65}{space 3}0.164{col 73}{space 4}-.0605235{col 86}{space 3} .0102586
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0183384{col 45}{space 2} .0171238{col 56}{space 1}    1.07{col 65}{space 3}0.284{col 73}{space 4}-.0152599{col 86}{space 3} .0519367
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0111983{col 45}{space 2} .0169202{col 56}{space 1}    0.66{col 65}{space 3}0.508{col 73}{space 4}-.0220005{col 86}{space 3} .0443972
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2}-.0162315{col 45}{space 2} .0164474{col 56}{space 1}   -0.99{col 65}{space 3}0.324{col 73}{space 4}-.0485027{col 86}{space 3} .0160397
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2}  .072482{col 45}{space 2} .0144821{col 56}{space 1}    5.00{col 65}{space 3}0.000{col 73}{space 4} .0440669{col 86}{space 3}  .100897
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4462335{col 45}{space 2} .0257498{col 56}{space 1}   17.33{col 65}{space 3}0.000{col 73}{space 4} .3957103{col 86}{space 3} .4967568
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Interactions
. reg plan_chosen (ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local)##i.strong_DR [pweight=weight], cluster(caseid)
{txt}(sum of wgt is 28,334.2855099389)

Linear regression                               Number of obs     = {res}    27,390
                                                {txt}F(41, 2738)       =  {res}     8.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0216
                                                {txt}Root MSE          =    {res} .49496

{txt}{ralign 100:(Std. Err. adjusted for {res:2,739} clusters in caseid)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                       plan_chosen{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 30}educ {c |}
{space 18}Charter schools  {c |}{col 36}{res}{space 2}-.0450884{col 48}{space 2}  .016147{col 59}{space 1}   -2.79{col 68}{space 3}0.005{col 76}{space 4}-.0767498{col 89}{space 3}-.0134269
{txt}{space 14}Vouchers to schools  {c |}{col 36}{res}{space 2}-.0381262{col 48}{space 2} .0166889{col 59}{space 1}   -2.28{col 68}{space 3}0.022{col 76}{space 4}-.0708504{col 89}{space 3} -.005402
{txt}{space 18}Free pre-school  {c |}{col 36}{res}{space 2} .0524119{col 48}{space 2} .0158747{col 59}{space 1}    3.30{col 68}{space 3}0.001{col 76}{space 4} .0212843{col 89}{space 3} .0835395
{txt}{space 16}Pay teachers more  {c |}{col 36}{res}{space 2}  .075387{col 48}{space 2} .0158241{col 59}{space 1}    4.76{col 68}{space 3}0.000{col 76}{space 4} .0443587{col 89}{space 3} .1064154
{txt}{space 34} {c |}
{space 28}hieduc {c |}
{space 15}Community colleges  {c |}{col 36}{res}{space 2} .0264617{col 48}{space 2}  .016575{col 59}{space 1}    1.60{col 68}{space 3}0.110{col 76}{space 4}-.0060391{col 89}{space 3} .0589624
{txt}{space 8}Local public universities  {c |}{col 36}{res}{space 2} .0088563{col 48}{space 2} .0157178{col 59}{space 1}    0.56{col 68}{space 3}0.573{col 76}{space 4}-.0219636{col 89}{space 3} .0396763
{txt}{space 4}Technical vocational training  {c |}{col 36}{res}{space 2} .0400955{col 48}{space 2} .0159224{col 59}{space 1}    2.52{col 68}{space 3}0.012{col 76}{space 4} .0088743{col 89}{space 3} .0713166
{txt}{space 11}Student grant programs  {c |}{col 36}{res}{space 2} .0636447{col 48}{space 2} .0169778{col 59}{space 1}    3.75{col 68}{space 3}0.000{col 76}{space 4} .0303541{col 89}{space 3} .0969353
{txt}{space 34} {c |}
{space 28}invest {c |}
{space 11}Attract new businesses  {c |}{col 36}{res}{space 2} .0289499{col 48}{space 2} .0135919{col 59}{space 1}    2.13{col 68}{space 3}0.033{col 76}{space 4} .0022985{col 89}{space 3} .0556014
{txt}{space 5}Stimulate existing companies  {c |}{col 36}{res}{space 2} .0221864{col 48}{space 2} .0143539{col 59}{space 1}    1.55{col 68}{space 3}0.122{col 76}{space 4}-.0059592{col 89}{space 3}  .050332
{txt}{space 3}Encourage investment charities  {c |}{col 36}{res}{space 2} .0133458{col 48}{space 2}   .01506{col 59}{space 1}    0.89{col 68}{space 3}0.376{col 76}{space 4}-.0161843{col 89}{space 3} .0428759
{txt}{space 34} {c |}
{space 31}gov {c |}
{space 5}Consolidate local government  {c |}{col 36}{res}{space 2} .0162986{col 48}{space 2} .0127923{col 59}{space 1}    1.27{col 68}{space 3}0.203{col 76}{space 4} -.008785{col 89}{space 3} .0413822
{txt}{space 10}More power to the state  {c |}{col 36}{res}{space 2}-.0051973{col 48}{space 2} .0130325{col 59}{space 1}   -0.40{col 68}{space 3}0.690{col 76}{space 4}-.0307518{col 89}{space 3} .0203572
{txt}{space 34} {c |}
{space 27}workers {c |}
{space 14}Limit unions' power  {c |}{col 36}{res}{space 2}-.0782062{col 48}{space 2} .0166205{col 59}{space 1}   -4.71{col 68}{space 3}0.000{col 76}{space 4}-.1107963{col 89}{space 3}-.0456161
{txt}{space 13}Expand unions' power  {c |}{col 36}{res}{space 2} .0103207{col 48}{space 2} .0154803{col 59}{space 1}    0.67{col 68}{space 3}0.505{col 76}{space 4}-.0200335{col 89}{space 3} .0406749
{txt}{space 9}Worker training vouchers  {c |}{col 36}{res}{space 2} .0227133{col 48}{space 2} .0158565{col 59}{space 1}    1.43{col 68}{space 3}0.152{col 76}{space 4}-.0083786{col 89}{space 3} .0538052
{txt}{space 6}Tax breaks to entrepreneurs  {c |}{col 36}{res}{space 2} .0361211{col 48}{space 2} .0160911{col 59}{space 1}    2.24{col 68}{space 3}0.025{col 76}{space 4} .0045692{col 89}{space 3} .0676729
{txt}{space 34} {c |}
{space 29}local {c |}
{space 15}Affordable housing  {c |}{col 36}{res}{space 2} .0621165{col 48}{space 2} .0145651{col 59}{space 1}    4.26{col 68}{space 3}0.000{col 76}{space 4} .0335568{col 89}{space 3} .0906761
{txt}{space 12}Public transportation  {c |}{col 36}{res}{space 2} .0195876{col 48}{space 2}  .014562{col 59}{space 1}    1.35{col 68}{space 3}0.179{col 76}{space 4} -.008966{col 89}{space 3} .0481411
{txt}{space 6}Safety and crime prevention  {c |}{col 36}{res}{space 2} .0147954{col 48}{space 2} .0147964{col 59}{space 1}    1.00{col 68}{space 3}0.317{col 76}{space 4}-.0142179{col 89}{space 3} .0438087
{txt}{space 34} {c |}
{space 25}strong_DR {c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0257671{col 48}{space 2}  .034832{col 59}{space 1}    0.74{col 68}{space 3}0.460{col 76}{space 4}-.0425326{col 89}{space 3} .0940667
{txt}{space 34} {c |}
{space 20}educ#strong_DR {c |}
Charter schools#Strong Republican  {c |}{col 36}{res}{space 2} .0907021{col 48}{space 2} .0262247{col 59}{space 1}    3.46{col 68}{space 3}0.001{col 76}{space 4} .0392799{col 89}{space 3} .1421243
{txt}{space 14}Vouchers to schools #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0933496{col 48}{space 2} .0251126{col 59}{space 1}    3.72{col 68}{space 3}0.000{col 76}{space 4}  .044108{col 89}{space 3} .1425912
{txt}Free pre-school#Strong Republican  {c |}{col 36}{res}{space 2} -.072354{col 48}{space 2} .0236345{col 59}{space 1}   -3.06{col 68}{space 3}0.002{col 76}{space 4}-.1186971{col 89}{space 3}-.0260108
{txt}{space 16}Pay teachers more #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0394459{col 48}{space 2} .0236141{col 59}{space 1}   -1.67{col 68}{space 3}0.095{col 76}{space 4}-.0857491{col 89}{space 3} .0068573
{txt}{space 34} {c |}
{space 18}hieduc#strong_DR {c |}
{space 15}Community colleges #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0084666{col 48}{space 2} .0241752{col 59}{space 1}    0.35{col 68}{space 3}0.726{col 76}{space 4}-.0389369{col 89}{space 3} .0558701
{txt}{space 8}Local public universities #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0108485{col 48}{space 2} .0236582{col 59}{space 1}   -0.46{col 68}{space 3}0.647{col 76}{space 4}-.0572383{col 89}{space 3} .0355412
{txt}{space 4}Technical vocational training #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0063908{col 48}{space 2} .0243213{col 59}{space 1}   -0.26{col 68}{space 3}0.793{col 76}{space 4}-.0540807{col 89}{space 3} .0412991
{txt}{space 11}Student grant programs #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0560559{col 48}{space 2} .0247185{col 59}{space 1}   -2.27{col 68}{space 3}0.023{col 76}{space 4}-.1045246{col 89}{space 3}-.0075872
{txt}{space 34} {c |}
{space 18}invest#strong_DR {c |}
{space 11}Attract new businesses #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0279639{col 48}{space 2} .0204933{col 59}{space 1}    1.36{col 68}{space 3}0.173{col 76}{space 4}  -.01222{col 89}{space 3} .0681478
{txt}{space 5}Stimulate existing companies #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0341464{col 48}{space 2} .0215533{col 59}{space 1}    1.58{col 68}{space 3}0.113{col 76}{space 4} -.008116{col 89}{space 3} .0764087
{txt}{space 3}Encourage investment charities #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0156763{col 48}{space 2}  .020966{col 59}{space 1}   -0.75{col 68}{space 3}0.455{col 76}{space 4}-.0567871{col 89}{space 3} .0254346
{txt}{space 34} {c |}
{space 21}gov#strong_DR {c |}
{space 5}Consolidate local government #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0045801{col 48}{space 2} .0195328{col 59}{space 1}   -0.23{col 68}{space 3}0.815{col 76}{space 4}-.0428806{col 89}{space 3} .0337204
{txt}{space 10}More power to the state #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0040733{col 48}{space 2} .0191724{col 59}{space 1}    0.21{col 68}{space 3}0.832{col 76}{space 4}-.0335204{col 89}{space 3} .0416671
{txt}{space 34} {c |}
{space 17}workers#strong_DR {c |}
{space 14}Limit unions' power #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0520912{col 48}{space 2} .0246645{col 59}{space 1}    2.11{col 68}{space 3}0.035{col 76}{space 4} .0037284{col 89}{space 3} .1004541
{txt}{space 13}Expand unions' power #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.1421406{col 48}{space 2} .0240687{col 59}{space 1}   -5.91{col 68}{space 3}0.000{col 76}{space 4}-.1893352{col 89}{space 3}-.0949459
{txt}{space 9}Worker training vouchers #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0478458{col 48}{space 2} .0240107{col 59}{space 1}   -1.99{col 68}{space 3}0.046{col 76}{space 4}-.0949267{col 89}{space 3}-.0007648
{txt}{space 6}Tax breaks to entrepreneurs #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0177827{col 48}{space 2} .0234927{col 59}{space 1}   -0.76{col 68}{space 3}0.449{col 76}{space 4}-.0638479{col 89}{space 3} .0282825
{txt}{space 34} {c |}
{space 19}local#strong_DR {c |}
{space 15}Affordable housing #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0509181{col 48}{space 2} .0223204{col 59}{space 1}   -2.28{col 68}{space 3}0.023{col 76}{space 4}-.0946847{col 89}{space 3}-.0071515
{txt}{space 12}Public transportation #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2}-.0358191{col 48}{space 2} .0219624{col 59}{space 1}   -1.63{col 68}{space 3}0.103{col 76}{space 4}-.0788837{col 89}{space 3} .0072455
{txt}{space 6}Safety and crime prevention #{c |}
{space 16}Strong Republican  {c |}{col 36}{res}{space 2} .0576865{col 48}{space 2} .0207001{col 59}{space 1}    2.79{col 68}{space 3}0.005{col 76}{space 4} .0170971{col 89}{space 3}  .098276
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4204664{col 48}{space 2} .0234682{col 59}{space 1}   17.92{col 68}{space 3}0.000{col 76}{space 4} .3744492{col 89}{space 3} .4664836
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using "~/Desktop/tableA6.tex", replace tex(frag) labe
{txt}{stata `"shellout using `"~/Desktop/tableA6.tex"'"':~/Desktop/tableA6.tex}
{browse `"/Users/wpmarble/Dropbox/Cities/Publication_Files/Data"' :dir}{com} : {txt}{stata `"seeout using "~/Desktop/tableA6.txt", label"':seeout}

{com}. 
. 
. 
. 
. 
. 
. ***** Conjoint for each MSA separately *****
. 
. *** Conjoint analysis by MSA
. foreach i of numlist 1/8 {c -(}
{txt}  2{com}. 
. *** Load analysis data
. use msa_survey_indiv, clear
{txt}  3{com}. 
. keep if msa==`i'
{txt}  4{com}. tab msa
{txt}  5{com}. 
. local forlab: value label msa
{txt}  6{com}. local label: label `forlab' `i'
{txt}  7{com}. di "`label'"
{txt}  8{com}. 
. *** Generate conjoint data 
. 
. keep caseid q16*
{txt}  9{com}. 
. * Number of dimensions: 6
. * Number of comparisons: 5
. * Number of plans per comparison: 2
. * Final conjoint data: 10 rows per respondent, with infomation about 6 dimensions and whether the plan was chosen
. 
. * Make 5 comparisions into rows per policy dimension
. foreach policy in educ hieduc invest gov workers local {c -(}
{txt} 10{com}. rename q16a_1_`policy' `policy'11
{txt} 11{com}. rename q16b_1_`policy' `policy'12
{txt} 12{com}. rename q16c_1_`policy' `policy'13
{txt} 13{com}. rename q16d_1_`policy' `policy'14
{txt} 14{com}. rename q16e_1_`policy' `policy'15
{txt} 15{com}. rename q16a_2_`policy' `policy'21
{txt} 16{com}. rename q16b_2_`policy' `policy'22
{txt} 17{com}. rename q16c_2_`policy' `policy'23
{txt} 18{com}. rename q16d_2_`policy' `policy'24
{txt} 19{com}. rename q16e_2_`policy' `policy'25
{txt} 20{com}. {c )-}
{txt} 21{com}. 
. reshape long educ1 hieduc1 invest1 gov1 workers1 local1 educ2 hieduc2 invest2 gov2 workers2 local2, i(caseid) j(table)
{txt} 22{com}. 
. * Make 2 rows for each 5 comparisons, for each 6 policy dimensions
. sort caseid table
{txt} 23{com}. g count=_n
{txt} 24{com}. 
. reshape long educ hieduc invest gov workers local, i(count) j(plan)
{txt} 25{com}. drop count
{txt} 26{com}. 
. rename q16_a table1_choice
{txt} 27{com}. rename q16_b table2_choice
{txt} 28{com}. rename q16_c table3_choice
{txt} 29{com}. rename q16_d table4_choice
{txt} 30{com}. rename q16_e table5_choice
{txt} 31{com}. 
. g plan_chosen=.
{txt} 32{com}. forval t=1/5 {c -(}
{txt} 33{com}. replace plan_chosen=1 if table==`t' & plan==1 & table`t'_choice==1 
{txt} 34{com}. replace plan_chosen=0 if table==`t' & plan==1 & table`t'_choice==2
{txt} 35{com}. replace plan_chosen=1 if table==`t' & plan==2 & table`t'_choice==2 
{txt} 36{com}. replace plan_chosen=0 if table==`t' & plan==2 & table`t'_choice==1 
{txt} 37{com}. {c )-}
{txt} 38{com}. 
. drop table1_choice- table5_choice
{txt} 39{com}. 
. 
. *** Merge with other information from the survey and save MSA data
. 
. merge m:1 caseid using msa_survey_indiv
{txt} 40{com}. drop _merge
{txt} 41{com}. keep if msa==`i'
{txt} 42{com}. 
. save "`label'_conjoint.dta", replace
{txt} 43{com}. {c )-}
{txt}( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   Charlotte {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Charlotte
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1000   {txt}->{res}    5000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5000   {txt}->{res}   10000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(10,000 missing values generated)
(527 real changes made)
(473 real changes made)
(473 real changes made)
(527 real changes made)
(527 real changes made)
(473 real changes made)
(473 real changes made)
(527 real changes made)
(530 real changes made)
(470 real changes made)
(470 real changes made)
(530 real changes made)
(520 real changes made)
(480 real changes made)
(480 real changes made)
(520 real changes made)
(518 real changes made)
(482 real changes made)
(482 real changes made)
(518 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,800
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,800{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}
(6,800 observations deleted)
file Charlotte_conjoint.dta saved
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   Cleveland {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Cleveland
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1000   {txt}->{res}    5000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5000   {txt}->{res}   10000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(10,000 missing values generated)
(527 real changes made)
(473 real changes made)
(473 real changes made)
(527 real changes made)
(532 real changes made)
(468 real changes made)
(468 real changes made)
(532 real changes made)
(533 real changes made)
(467 real changes made)
(467 real changes made)
(533 real changes made)
(515 real changes made)
(485 real changes made)
(485 real changes made)
(515 real changes made)
(524 real changes made)
(476 real changes made)
(476 real changes made)
(524 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,800
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,800{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}
(6,800 observations deleted)
file Cleveland_conjoint.dta saved
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Houston {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Houston
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1000   {txt}->{res}    5000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5000   {txt}->{res}   10000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(10,000 missing values generated)
(533 real changes made)
(467 real changes made)
(467 real changes made)
(533 real changes made)
(517 real changes made)
(483 real changes made)
(483 real changes made)
(517 real changes made)
(514 real changes made)
(486 real changes made)
(486 real changes made)
(514 real changes made)
(545 real changes made)
(455 real changes made)
(455 real changes made)
(545 real changes made)
(498 real changes made)
(502 real changes made)
(502 real changes made)
(498 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,800
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,800{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}
(6,800 observations deleted)
file Houston_conjoint.dta saved
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Indianapolis {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Indianapolis
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1000   {txt}->{res}    5000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5000   {txt}->{res}   10000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(10,000 missing values generated)
(485 real changes made)
(515 real changes made)
(515 real changes made)
(485 real changes made)
(499 real changes made)
(501 real changes made)
(501 real changes made)
(499 real changes made)
(496 real changes made)
(504 real changes made)
(504 real changes made)
(496 real changes made)
(517 real changes made)
(483 real changes made)
(483 real changes made)
(517 real changes made)
(514 real changes made)
(486 real changes made)
(486 real changes made)
(514 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,800
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,800{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}
(6,800 observations deleted)
file Indianapolis_conjoint.dta saved
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Memphis {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Memphis
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1000   {txt}->{res}    5000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5000   {txt}->{res}   10000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(10,000 missing values generated)
(550 real changes made)
(450 real changes made)
(450 real changes made)
(550 real changes made)
(513 real changes made)
(487 real changes made)
(487 real changes made)
(513 real changes made)
(504 real changes made)
(496 real changes made)
(496 real changes made)
(504 real changes made)
(498 real changes made)
(502 real changes made)
(502 real changes made)
(498 real changes made)
(534 real changes made)
(466 real changes made)
(466 real changes made)
(534 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,800
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,800{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}
(6,800 observations deleted)
file Memphis_conjoint.dta saved
( )
(7,000 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   Rochester {c |}{res}        800      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        800      100.00
Rochester
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}     800   {txt}->{res}    4000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    4000   {txt}->{res}    8000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(8,000 missing values generated)
(435 real changes made)
(365 real changes made)
(365 real changes made)
(435 real changes made)
(419 real changes made)
(381 real changes made)
(381 real changes made)
(419 real changes made)
(410 real changes made)
(390 real changes made)
(390 real changes made)
(410 real changes made)
(419 real changes made)
(381 real changes made)
(381 real changes made)
(419 real changes made)
(447 real changes made)
(353 real changes made)
(353 real changes made)
(447 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           7,000
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           7,000{txt}  (_merge==2)

{col 5}matched{col 30}{res}           8,000{txt}  (_merge==3)
{col 5}{hline 41}
(7,000 observations deleted)
file Rochester_conjoint.dta saved
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
   St. Louis {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
St. Louis
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1000   {txt}->{res}    5000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5000   {txt}->{res}   10000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(10,000 missing values generated)
(534 real changes made)
(466 real changes made)
(466 real changes made)
(534 real changes made)
(511 real changes made)
(489 real changes made)
(489 real changes made)
(511 real changes made)
(526 real changes made)
(474 real changes made)
(474 real changes made)
(526 real changes made)
(541 real changes made)
(459 real changes made)
(459 real changes made)
(541 real changes made)
(526 real changes made)
(474 real changes made)
(474 real changes made)
(526 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,800
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,800{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}
(6,800 observations deleted)
file St. Louis_conjoint.dta saved
( )
(6,800 observations deleted)

         msa {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Seattle {c |}{res}      1,000      100.00      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,000      100.00
Seattle
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1000   {txt}->{res}    5000
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}
(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5000   {txt}->{res}   10000
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}
{res}{txt}(10,000 missing values generated)
(528 real changes made)
(472 real changes made)
(472 real changes made)
(528 real changes made)
(506 real changes made)
(494 real changes made)
(494 real changes made)
(506 real changes made)
(530 real changes made)
(470 real changes made)
(470 real changes made)
(530 real changes made)
(535 real changes made)
(465 real changes made)
(465 real changes made)
(535 real changes made)
(510 real changes made)
(490 real changes made)
(490 real changes made)
(510 real changes made)
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,800
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,800{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}
(6,800 observations deleted)
file Seattle_conjoint.dta saved

{com}. 
. 
. *** Graphs
. 
. * All individual city estimates in one graph
. use "Charlotte_conjoint.dta", clear
{txt}( )

{com}. append using "Cleveland_conjoint.dta" "Houston_conjoint.dta" "Indianapolis_conjoint.dta" ///
> "Memphis_conjoint.dta" "Rochester_conjoint.dta" "St. Louis_conjoint.dta" "Seattle_conjoint.dta", generate(data)
{txt}(label med_trustinst2 already defined)
(label med_asc2 already defined)
(label college already defined)
(label quar14 already defined)
(label terc13Income already defined)
(label terc13 already defined)
(label med_income already defined)
(label med_trustinst already defined)
(label med_racial already defined)
(label med_asc already defined)
(label trustpeople already defined)
(label highlow already defined)
(label wf already defined)
(label single already defined)
(label children already defined)
(label gender already defined)
(label cons already defined)
(label dummy already defined)
(label hardworkthen already defined)
(label hardwork already defined)
(label temp12 already defined)
(label temp11 already defined)
(label temp10 already defined)
(label temp9 already defined)
(label temp8 already defined)
(label temp7 already defined)
(label temp6 already defined)
(label temp5 already defined)
(label temp4 already defined)
(label temp3 already defined)
(label temp2 already defined)
(label temp1 already defined)
(label trust_change already defined)
(label trust already defined)
(label trustp already defined)
(label vimp already defined)
(label satisfaction already defined)
(label immigop already defined)
(label safety already defined)
(label ineqsmall already defined)
(label agree already defined)
(label Q66 already defined)
(label RACIALD already defined)
(label RACIALC already defined)
(label RACIALB already defined)
(label RACIALA already defined)
(label Q62 already defined)
(label Q61 already defined)
(label Q60 already defined)
(label Q59 already defined)
(label Q57 already defined)
(label Q56 already defined)
(label Q55_8_GE already defined)
(label Q55_7_GE already defined)
(label Q55_6_GE already defined)
(label Q55_5_GE already defined)
(label Q55_4_GE already defined)
(label Q55_3_GE already defined)
(label Q55_2_GE already defined)
(label Q55_1_GE already defined)
(label Q54 already defined)
(label Q53 already defined)
(label Q52 already defined)
(label Q49 already defined)
(label Q48 already defined)
(label Q46_OTHE already defined)
(label Q46 already defined)
(label Q43 already defined)
(label Q42 already defined)
(label Q41_B already defined)
(label Q41_A already defined)
(label Q41 already defined)
(label Q37 already defined)
(label Q36G already defined)
(label Q36F already defined)
(label Q36E already defined)
(label Q36D already defined)
(label Q36C already defined)
(label Q36B already defined)
(label Q36A already defined)
(label Q31R already defined)
(label Q31Q already defined)
(label Q31P already defined)
(label Q31O already defined)
(label Q31N already defined)
(label Q31M already defined)
(label Q31L already defined)
(label Q31K already defined)
(label Q31J already defined)
(label Q31I already defined)
(label Q31H already defined)
(label Q31G already defined)
(label Q31F already defined)
(label Q31E already defined)
(label Q31D already defined)
(label Q31C already defined)
(label Q31B already defined)
(label Q31A already defined)
(label Q30 already defined)
(label Q29 already defined)
(label V120_A already defined)
(label V119_A already defined)
(label V118_A already defined)
(label V117_A already defined)
(label V116_A already defined)
(label Q22_TEST already defined)
(label Q20 already defined)
(label Q19 already defined)
(label Q16_E already defined)
(label Q16_D already defined)
(label Q16_C already defined)
(label Q16_B already defined)
(label Q16_A already defined)
(label Q16E_2_L already defined)
(label Q16E_1_L already defined)
(label Q16E_2_W already defined)
(label Q16E_1_W already defined)
(label Q16E_2_G already defined)
(label Q16E_1_G already defined)
(label Q16E_2_I already defined)
(label Q16E_1_I already defined)
(label Q16E_2_H already defined)
(label Q16E_1_H already defined)
(label Q16E_2_E already defined)
(label Q16E_1_E already defined)
(label Q16D_2_L already defined)
(label Q16D_1_L already defined)
(label Q16D_2_W already defined)
(label Q16D_1_W already defined)
(label Q16D_2_G already defined)
(label Q16D_1_G already defined)
(label Q16D_2_I already defined)
(label Q16D_1_I already defined)
(label Q16D_2_H already defined)
(label Q16D_1_H already defined)
(label Q16D_2_E already defined)
(label Q16D_1_E already defined)
(label Q16C_2_L already defined)
(label Q16C_1_L already defined)
(label Q16C_2_W already defined)
(label Q16C_1_W already defined)
(label Q16C_2_G already defined)
(label Q16C_1_G already defined)
(label Q16C_2_I already defined)
(label Q16C_1_I already defined)
(label Q16C_2_H already defined)
(label Q16C_1_H already defined)
(label Q16C_2_E already defined)
(label Q16C_1_E already defined)
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(label med_trustinst2 already defined)
(label med_asc2 already defined)
(label college already defined)
(label quar14 already defined)
(label terc13Income already defined)
(label terc13 already defined)
(label med_income already defined)
(label med_trustinst already defined)
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(label trust_change already defined)
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(label trustp already defined)
(label vimp already defined)
(label satisfaction already defined)
(label immigop already defined)
(label safety already defined)
(label ineqsmall already defined)
(label agree already defined)
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(label college already defined)
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(label terc13Income already defined)
(label terc13 already defined)
(label med_income already defined)
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(label single already defined)
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{com}. set scheme s1mono // Graph layout
{txt}
{com}. 
. * Short value labels
. lab def educ 1 "Charter schools" 2 "Vouchers to schools" 3 "Free pre-school" 4 "Pay teachers more" 5 "Keep current" 
{txt}
{com}. lab def hieduc 1 "Community colleges" 2 "Local public universities" 3 "Technical vocational training" 4 "Student grant programs" 5 "Keep current" 
{txt}
{com}. lab def invest 1 "Attract new businesses" 2 "Stimulate existing companies" 3 "Encourage investment charities" 4 "Keep current" 
{txt}
{com}. lab def gov 1 "Consolidate local government" 2 "More power to the state" 3 "Keep current"
{txt}
{com}. lab def workers 1 "Limit unions' power" 2 "Expand unions' power" 3 "Worker training vouchers" 4 "Tax breaks to entrepreneurs" 5 "Keep current"
{txt}
{com}. lab def local 1 "Affordable housing" 2 "Public transportation" 3 "Safety and crime prevention" 4 "Keep current"   
{txt}
{com}. lab values educ educ
{txt}
{com}. lab values hieduc hieduc
{txt}
{com}. lab values invest invest
{txt}
{com}. lab values gov gov
{txt}
{com}. lab values workers workers
{txt}
{com}. lab values local local
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==0, cluster(caseid)
{txt}(sum of wgt is 10,000)

Linear regression                               Number of obs     = {res}    10,000
                                                {txt}F(20, 999)        =  {res}     5.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0188
                                                {txt}Root MSE          =    {res}  .4958

{txt}{ralign 97:(Std. Err. adjusted for {res:1,000} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2} .0246953{col 45}{space 2} .0216503{col 56}{space 1}    1.14{col 65}{space 3}0.254{col 73}{space 4}  -.01779{col 86}{space 3} .0671806
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2}  .077228{col 45}{space 2} .0217543{col 56}{space 1}    3.55{col 65}{space 3}0.000{col 73}{space 4} .0345387{col 86}{space 3} .1199173
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2}  .068526{col 45}{space 2} .0200867{col 56}{space 1}    3.41{col 65}{space 3}0.001{col 73}{space 4}  .029109{col 86}{space 3} .1079429
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0883344{col 45}{space 2} .0211345{col 56}{space 1}    4.18{col 65}{space 3}0.000{col 73}{space 4} .0468613{col 86}{space 3} .1298076
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0463887{col 45}{space 2} .0169088{col 56}{space 1}    2.74{col 65}{space 3}0.006{col 73}{space 4} .0132079{col 86}{space 3} .0795694
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0437413{col 45}{space 2} .0187503{col 56}{space 1}    2.33{col 65}{space 3}0.020{col 73}{space 4} .0069469{col 86}{space 3} .0805357
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0194453{col 45}{space 2} .0207559{col 56}{space 1}    0.94{col 65}{space 3}0.349{col 73}{space 4}-.0212849{col 86}{space 3} .0601755
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0258586{col 45}{space 2} .0194891{col 56}{space 1}    1.33{col 65}{space 3}0.185{col 73}{space 4}-.0123856{col 86}{space 3} .0641029
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0422778{col 45}{space 2} .0175799{col 56}{space 1}    2.40{col 65}{space 3}0.016{col 73}{space 4} .0077801{col 86}{space 3} .0767755
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0369881{col 45}{space 2}  .016575{col 56}{space 1}    2.23{col 65}{space 3}0.026{col 73}{space 4} .0044624{col 86}{space 3} .0695139
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2}-.0053142{col 45}{space 2} .0205174{col 56}{space 1}   -0.26{col 65}{space 3}0.796{col 73}{space 4}-.0455763{col 86}{space 3}  .034948
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0086173{col 45}{space 2} .0174286{col 56}{space 1}    0.49{col 65}{space 3}0.621{col 73}{space 4}-.0255836{col 86}{space 3} .0428182
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0007517{col 45}{space 2} .0177931{col 56}{space 1}   -0.04{col 65}{space 3}0.966{col 73}{space 4}-.0356678{col 86}{space 3} .0341645
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0492242{col 45}{space 2} .0223193{col 56}{space 1}   -2.21{col 65}{space 3}0.028{col 73}{space 4}-.0930223{col 86}{space 3}-.0054261
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0787587{col 45}{space 2} .0212963{col 56}{space 1}   -3.70{col 65}{space 3}0.000{col 73}{space 4}-.1205493{col 86}{space 3}-.0369681
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0150308{col 45}{space 2} .0200749{col 56}{space 1}    0.75{col 65}{space 3}0.454{col 73}{space 4} -.024363{col 86}{space 3} .0544246
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0375702{col 45}{space 2} .0201704{col 56}{space 1}    1.86{col 65}{space 3}0.063{col 73}{space 4} -.002011{col 86}{space 3} .0771514
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0583202{col 45}{space 2} .0173711{col 56}{space 1}    3.36{col 65}{space 3}0.001{col 73}{space 4} .0242322{col 86}{space 3} .0924082
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2}  .006766{col 45}{space 2} .0181426{col 56}{space 1}    0.37{col 65}{space 3}0.709{col 73}{space 4}-.0288359{col 86}{space 3}  .042368
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0652027{col 45}{space 2} .0188077{col 56}{space 1}    3.47{col 65}{space 3}0.001{col 73}{space 4} .0282956{col 86}{space 3} .1021098
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .3827068{col 45}{space 2} .0295278{col 56}{space 1}   12.96{col 65}{space 3}0.000{col 73}{space 4}  .324763{col 86}{space 3} .4406505
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w0
{txt}
{com}. local n_baseline0 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==1, cluster(caseid)
{txt}(sum of wgt is 10,000)

Linear regression                               Number of obs     = {res}    10,000
                                                {txt}F(20, 999)        =  {res}     4.03
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0117
                                                {txt}Root MSE          =    {res}  .4976

{txt}{ralign 97:(Std. Err. adjusted for {res:1,000} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0590762{col 45}{space 2} .0177557{col 56}{space 1}   -3.33{col 65}{space 3}0.001{col 73}{space 4} -.093919{col 86}{space 3}-.0242335
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2}-.0244902{col 45}{space 2} .0199868{col 56}{space 1}   -1.23{col 65}{space 3}0.221{col 73}{space 4}-.0637111{col 86}{space 3} .0147307
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0024907{col 45}{space 2} .0177281{col 56}{space 1}    0.14{col 65}{space 3}0.888{col 73}{space 4}-.0322979{col 86}{space 3} .0372793
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0244519{col 45}{space 2} .0198659{col 56}{space 1}    1.23{col 65}{space 3}0.219{col 73}{space 4}-.0145318{col 86}{space 3} .0634357
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0272794{col 45}{space 2} .0184179{col 56}{space 1}    1.48{col 65}{space 3}0.139{col 73}{space 4}-.0088628{col 86}{space 3} .0634215
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0094589{col 45}{space 2} .0192691{col 56}{space 1}    0.49{col 65}{space 3}0.624{col 73}{space 4}-.0283537{col 86}{space 3} .0472714
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0569195{col 45}{space 2}  .019003{col 56}{space 1}    3.00{col 65}{space 3}0.003{col 73}{space 4} .0196291{col 86}{space 3} .0942099
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0548889{col 45}{space 2} .0191801{col 56}{space 1}    2.86{col 65}{space 3}0.004{col 73}{space 4}  .017251{col 86}{space 3} .0925268
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2}  .055472{col 45}{space 2} .0190883{col 56}{space 1}    2.91{col 65}{space 3}0.004{col 73}{space 4} .0180143{col 86}{space 3} .0929297
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0499599{col 45}{space 2} .0177125{col 56}{space 1}    2.82{col 65}{space 3}0.005{col 73}{space 4} .0152019{col 86}{space 3} .0847179
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0260493{col 45}{space 2} .0169702{col 56}{space 1}    1.53{col 65}{space 3}0.125{col 73}{space 4}-.0072522{col 86}{space 3} .0593507
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2}  .008788{col 45}{space 2} .0166756{col 56}{space 1}    0.53{col 65}{space 3}0.598{col 73}{space 4}-.0239352{col 86}{space 3} .0415112
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0188464{col 45}{space 2} .0155796{col 56}{space 1}   -1.21{col 65}{space 3}0.227{col 73}{space 4}-.0494189{col 86}{space 3} .0117261
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0364386{col 45}{space 2} .0199958{col 56}{space 1}   -1.82{col 65}{space 3}0.069{col 73}{space 4}-.0756772{col 86}{space 3}    .0028
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0058369{col 45}{space 2} .0210275{col 56}{space 1}   -0.28{col 65}{space 3}0.781{col 73}{space 4}-.0471001{col 86}{space 3} .0354263
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0224097{col 45}{space 2} .0192154{col 56}{space 1}    1.17{col 65}{space 3}0.244{col 73}{space 4}-.0152975{col 86}{space 3} .0601168
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0237491{col 45}{space 2} .0200942{col 56}{space 1}    1.18{col 65}{space 3}0.238{col 73}{space 4}-.0156825{col 86}{space 3} .0631808
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0449795{col 45}{space 2} .0157793{col 56}{space 1}    2.85{col 65}{space 3}0.004{col 73}{space 4} .0140151{col 86}{space 3}  .075944
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0188395{col 45}{space 2} .0161188{col 56}{space 1}    1.17{col 65}{space 3}0.243{col 73}{space 4}-.0127911{col 86}{space 3} .0504702
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0539124{col 45}{space 2} .0168567{col 56}{space 1}    3.20{col 65}{space 3}0.001{col 73}{space 4} .0208338{col 86}{space 3}  .086991
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4216328{col 45}{space 2} .0264628{col 56}{space 1}   15.93{col 65}{space 3}0.000{col 73}{space 4} .3697038{col 86}{space 3} .4735617
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w1
{txt}
{com}. local n_baseline1 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==2, cluster(caseid)
{txt}(sum of wgt is 10,000)

Linear regression                               Number of obs     = {res}    10,000
                                                {txt}F(20, 999)        =  {res}     4.66
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0157
                                                {txt}Root MSE          =    {res} .49659

{txt}{ralign 97:(Std. Err. adjusted for {res:1,000} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0243744{col 45}{space 2} .0209924{col 56}{space 1}   -1.16{col 65}{space 3}0.246{col 73}{space 4}-.0655687{col 86}{space 3} .0168199
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2}-.0017931{col 45}{space 2} .0203577{col 56}{space 1}   -0.09{col 65}{space 3}0.930{col 73}{space 4}-.0417419{col 86}{space 3} .0381557
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2}-.0124627{col 45}{space 2} .0202363{col 56}{space 1}   -0.62{col 65}{space 3}0.538{col 73}{space 4}-.0521732{col 86}{space 3} .0272479
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0469857{col 45}{space 2} .0206585{col 56}{space 1}    2.27{col 65}{space 3}0.023{col 73}{space 4} .0064466{col 86}{space 3} .0875248
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0502512{col 45}{space 2} .0205728{col 56}{space 1}    2.44{col 65}{space 3}0.015{col 73}{space 4} .0098804{col 86}{space 3}  .090622
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0278241{col 45}{space 2} .0194078{col 56}{space 1}    1.43{col 65}{space 3}0.152{col 73}{space 4}-.0102607{col 86}{space 3}  .065909
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0425394{col 45}{space 2} .0189545{col 56}{space 1}    2.24{col 65}{space 3}0.025{col 73}{space 4} .0053441{col 86}{space 3} .0797346
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2}  .024007{col 45}{space 2} .0200236{col 56}{space 1}    1.20{col 65}{space 3}0.231{col 73}{space 4}-.0152861{col 86}{space 3} .0633001
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0504584{col 45}{space 2} .0186187{col 56}{space 1}    2.71{col 65}{space 3}0.007{col 73}{space 4} .0139221{col 86}{space 3} .0869947
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0513204{col 45}{space 2} .0187298{col 56}{space 1}    2.74{col 65}{space 3}0.006{col 73}{space 4} .0145661{col 86}{space 3} .0880746
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2}-.0040497{col 45}{space 2} .0220177{col 56}{space 1}   -0.18{col 65}{space 3}0.854{col 73}{space 4} -.047256{col 86}{space 3} .0391566
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} -.007614{col 45}{space 2} .0161248{col 56}{space 1}   -0.47{col 65}{space 3}0.637{col 73}{space 4}-.0392565{col 86}{space 3} .0240284
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0459679{col 45}{space 2} .0160481{col 56}{space 1}   -2.86{col 65}{space 3}0.004{col 73}{space 4}-.0774597{col 86}{space 3}-.0144761
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2} -.024645{col 45}{space 2} .0199305{col 56}{space 1}   -1.24{col 65}{space 3}0.217{col 73}{space 4}-.0637554{col 86}{space 3} .0144654
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0412902{col 45}{space 2} .0219334{col 56}{space 1}   -1.88{col 65}{space 3}0.060{col 73}{space 4} -.084331{col 86}{space 3} .0017506
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0259092{col 45}{space 2} .0208213{col 56}{space 1}    1.24{col 65}{space 3}0.214{col 73}{space 4}-.0149493{col 86}{space 3} .0667677
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0546148{col 45}{space 2} .0203755{col 56}{space 1}    2.68{col 65}{space 3}0.007{col 73}{space 4} .0146311{col 86}{space 3} .0945985
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0316686{col 45}{space 2} .0170798{col 56}{space 1}    1.85{col 65}{space 3}0.064{col 73}{space 4}-.0018478{col 86}{space 3}  .065185
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0242397{col 45}{space 2} .0184589{col 56}{space 1}    1.31{col 65}{space 3}0.189{col 73}{space 4} -.011983{col 86}{space 3} .0604623
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0716163{col 45}{space 2} .0166417{col 56}{space 1}    4.30{col 65}{space 3}0.000{col 73}{space 4} .0389596{col 86}{space 3} .1042729
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4265204{col 45}{space 2} .0295525{col 56}{space 1}   14.43{col 65}{space 3}0.000{col 73}{space 4} .3685282{col 86}{space 3} .4845126
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w2
{txt}
{com}. local n_baseline2 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==3, cluster(caseid)
{txt}(sum of wgt is 10,000)

Linear regression                               Number of obs     = {res}    10,000
                                                {txt}F(20, 999)        =  {res}     6.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0198
                                                {txt}Root MSE          =    {res} .49554

{txt}{ralign 97:(Std. Err. adjusted for {res:1,000} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0093176{col 45}{space 2} .0205785{col 56}{space 1}   -0.45{col 65}{space 3}0.651{col 73}{space 4}-.0496996{col 86}{space 3} .0310645
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0119575{col 45}{space 2} .0199321{col 56}{space 1}    0.60{col 65}{space 3}0.549{col 73}{space 4}-.0271562{col 86}{space 3} .0510711
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0229104{col 45}{space 2}  .019227{col 56}{space 1}    1.19{col 65}{space 3}0.234{col 73}{space 4}-.0148196{col 86}{space 3} .0606403
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0955327{col 45}{space 2} .0212852{col 56}{space 1}    4.49{col 65}{space 3}0.000{col 73}{space 4} .0537639{col 86}{space 3} .1373014
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0729952{col 45}{space 2} .0195323{col 56}{space 1}    3.74{col 65}{space 3}0.000{col 73}{space 4} .0346662{col 86}{space 3} .1113241
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0358399{col 45}{space 2} .0203183{col 56}{space 1}    1.76{col 65}{space 3}0.078{col 73}{space 4}-.0040315{col 86}{space 3} .0757114
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0770963{col 45}{space 2} .0196718{col 56}{space 1}    3.92{col 65}{space 3}0.000{col 73}{space 4} .0384936{col 86}{space 3} .1156991
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0812039{col 45}{space 2} .0211874{col 56}{space 1}    3.83{col 65}{space 3}0.000{col 73}{space 4} .0396269{col 86}{space 3} .1227808
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0723498{col 45}{space 2} .0169295{col 56}{space 1}    4.27{col 65}{space 3}0.000{col 73}{space 4} .0391283{col 86}{space 3} .1055712
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0450919{col 45}{space 2} .0174898{col 56}{space 1}    2.58{col 65}{space 3}0.010{col 73}{space 4}  .010771{col 86}{space 3} .0794129
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0087421{col 45}{space 2} .0159946{col 56}{space 1}    0.55{col 65}{space 3}0.585{col 73}{space 4}-.0226448{col 86}{space 3}  .040129
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0175957{col 45}{space 2}  .015014{col 56}{space 1}    1.17{col 65}{space 3}0.241{col 73}{space 4}-.0118668{col 86}{space 3} .0470582
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2} .0103304{col 45}{space 2} .0153176{col 56}{space 1}    0.67{col 65}{space 3}0.500{col 73}{space 4} -.019728{col 86}{space 3} .0403887
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0505058{col 45}{space 2} .0188699{col 56}{space 1}   -2.68{col 65}{space 3}0.008{col 73}{space 4} -.087535{col 86}{space 3}-.0134766
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0437844{col 45}{space 2} .0184454{col 56}{space 1}   -2.37{col 65}{space 3}0.018{col 73}{space 4}-.0799806{col 86}{space 3}-.0075883
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2}-.0060261{col 45}{space 2} .0203237{col 56}{space 1}   -0.30{col 65}{space 3}0.767{col 73}{space 4}-.0459081{col 86}{space 3} .0338558
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0511882{col 45}{space 2} .0197997{col 56}{space 1}    2.59{col 65}{space 3}0.010{col 73}{space 4} .0123344{col 86}{space 3} .0900421
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0295278{col 45}{space 2}  .018638{col 56}{space 1}    1.58{col 65}{space 3}0.113{col 73}{space 4}-.0070464{col 86}{space 3}  .066102
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2}-.0008319{col 45}{space 2} .0194037{col 56}{space 1}   -0.04{col 65}{space 3}0.966{col 73}{space 4}-.0389085{col 86}{space 3} .0372448
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0475903{col 45}{space 2}  .017571{col 56}{space 1}    2.71{col 65}{space 3}0.007{col 73}{space 4} .0131099{col 86}{space 3} .0820707
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .3717354{col 45}{space 2} .0279752{col 56}{space 1}   13.29{col 65}{space 3}0.000{col 73}{space 4} .3168385{col 86}{space 3} .4266324
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w3
{txt}
{com}. local n_baseline2 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==4, cluster(caseid)
{txt}(sum of wgt is 10,000)

Linear regression                               Number of obs     = {res}    10,000
                                                {txt}F(20, 999)        =  {res}     4.47
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0157
                                                {txt}Root MSE          =    {res} .49658

{txt}{ralign 97:(Std. Err. adjusted for {res:1,000} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0133625{col 45}{space 2} .0223706{col 56}{space 1}   -0.60{col 65}{space 3}0.550{col 73}{space 4}-.0572612{col 86}{space 3} .0305363
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0042494{col 45}{space 2} .0253907{col 56}{space 1}    0.17{col 65}{space 3}0.867{col 73}{space 4}-.0455757{col 86}{space 3} .0540746
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0329755{col 45}{space 2} .0232531{col 56}{space 1}    1.42{col 65}{space 3}0.156{col 73}{space 4}-.0126551{col 86}{space 3}  .078606
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0538164{col 45}{space 2} .0235139{col 56}{space 1}    2.29{col 65}{space 3}0.022{col 73}{space 4}  .007674{col 86}{space 3} .0999588
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0259031{col 45}{space 2} .0264052{col 56}{space 1}    0.98{col 65}{space 3}0.327{col 73}{space 4} -.025913{col 86}{space 3} .0777192
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0051427{col 45}{space 2}  .023255{col 56}{space 1}    0.22{col 65}{space 3}0.825{col 73}{space 4}-.0404916{col 86}{space 3} .0507771
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0438514{col 45}{space 2} .0246457{col 56}{space 1}    1.78{col 65}{space 3}0.076{col 73}{space 4} -.004512{col 86}{space 3} .0922147
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0483743{col 45}{space 2}  .023365{col 56}{space 1}    2.07{col 65}{space 3}0.039{col 73}{space 4} .0025241{col 86}{space 3} .0942244
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2}  .053615{col 45}{space 2} .0187055{col 56}{space 1}    2.87{col 65}{space 3}0.004{col 73}{space 4} .0169085{col 86}{space 3} .0903215
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2}  .031109{col 45}{space 2} .0213811{col 56}{space 1}    1.45{col 65}{space 3}0.146{col 73}{space 4} -.010848{col 86}{space 3}  .073066
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0199114{col 45}{space 2} .0224269{col 56}{space 1}    0.89{col 65}{space 3}0.375{col 73}{space 4}-.0240979{col 86}{space 3} .0639207
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0280948{col 45}{space 2} .0176262{col 56}{space 1}    1.59{col 65}{space 3}0.111{col 73}{space 4}-.0064937{col 86}{space 3} .0626834
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2} .0113867{col 45}{space 2} .0162454{col 56}{space 1}    0.70{col 65}{space 3}0.484{col 73}{space 4}-.0204924{col 86}{space 3} .0432657
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0562553{col 45}{space 2} .0222905{col 56}{space 1}   -2.52{col 65}{space 3}0.012{col 73}{space 4}-.0999968{col 86}{space 3}-.0125137
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0749858{col 45}{space 2} .0220173{col 56}{space 1}   -3.41{col 65}{space 3}0.001{col 73}{space 4}-.1181913{col 86}{space 3}-.0317803
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2}-.0133868{col 45}{space 2} .0232592{col 56}{space 1}   -0.58{col 65}{space 3}0.565{col 73}{space 4}-.0590294{col 86}{space 3} .0322557
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2}-.0018872{col 45}{space 2} .0222709{col 56}{space 1}   -0.08{col 65}{space 3}0.932{col 73}{space 4}-.0455903{col 86}{space 3} .0418159
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2}  .045575{col 45}{space 2} .0229943{col 56}{space 1}    1.98{col 65}{space 3}0.048{col 73}{space 4} .0004524{col 86}{space 3} .0906976
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2}-.0092863{col 45}{space 2}  .020934{col 56}{space 1}   -0.44{col 65}{space 3}0.657{col 73}{space 4} -.050366{col 86}{space 3} .0317935
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0847542{col 45}{space 2} .0234637{col 56}{space 1}    3.61{col 65}{space 3}0.000{col 73}{space 4} .0387105{col 86}{space 3} .1307979
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4204192{col 45}{space 2} .0352015{col 56}{space 1}   11.94{col 65}{space 3}0.000{col 73}{space 4} .3513418{col 86}{space 3} .4894965
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w4
{txt}
{com}. local n_baseline2 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==5, cluster(caseid)
{txt}(sum of wgt is 8,000)

Linear regression                               Number of obs     = {res}     8,000
                                                {txt}F(20, 799)        =  {res}     2.79
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0091
                                                {txt}Root MSE          =    {res} .49838

{txt}{ralign 97:(Std. Err. adjusted for {res:800} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2} .0168621{col 45}{space 2} .0205562{col 56}{space 1}    0.82{col 65}{space 3}0.412{col 73}{space 4}-.0234885{col 86}{space 3} .0572126
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0286993{col 45}{space 2} .0207069{col 56}{space 1}    1.39{col 65}{space 3}0.166{col 73}{space 4}-.0119469{col 86}{space 3} .0693456
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0279198{col 45}{space 2} .0179959{col 56}{space 1}    1.55{col 65}{space 3}0.121{col 73}{space 4}-.0074049{col 86}{space 3} .0632446
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0325091{col 45}{space 2} .0197017{col 56}{space 1}    1.65{col 65}{space 3}0.099{col 73}{space 4} -.006164{col 86}{space 3} .0711822
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0614128{col 45}{space 2} .0217019{col 56}{space 1}    2.83{col 65}{space 3}0.005{col 73}{space 4} .0188133{col 86}{space 3} .1040124
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2}  .038218{col 45}{space 2} .0230142{col 56}{space 1}    1.66{col 65}{space 3}0.097{col 73}{space 4}-.0069575{col 86}{space 3} .0833936
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0360322{col 45}{space 2} .0214786{col 56}{space 1}    1.68{col 65}{space 3}0.094{col 73}{space 4} -.006129{col 86}{space 3} .0781933
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0368564{col 45}{space 2} .0212308{col 56}{space 1}    1.74{col 65}{space 3}0.083{col 73}{space 4}-.0048183{col 86}{space 3}  .078531
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0479236{col 45}{space 2} .0207908{col 56}{space 1}    2.31{col 65}{space 3}0.021{col 73}{space 4} .0071125{col 86}{space 3} .0887346
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0213608{col 45}{space 2} .0199081{col 56}{space 1}    1.07{col 65}{space 3}0.284{col 73}{space 4}-.0177175{col 86}{space 3}  .060439
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0184689{col 45}{space 2} .0187072{col 56}{space 1}    0.99{col 65}{space 3}0.324{col 73}{space 4}-.0182522{col 86}{space 3} .0551901
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0027333{col 45}{space 2} .0161059{col 56}{space 1}    0.17{col 65}{space 3}0.865{col 73}{space 4}-.0288815{col 86}{space 3} .0343482
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0046964{col 45}{space 2} .0165055{col 56}{space 1}   -0.28{col 65}{space 3}0.776{col 73}{space 4}-.0370956{col 86}{space 3} .0277028
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0618119{col 45}{space 2} .0225153{col 56}{space 1}   -2.75{col 65}{space 3}0.006{col 73}{space 4}-.1060079{col 86}{space 3}-.0176158
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0581355{col 45}{space 2} .0213164{col 56}{space 1}   -2.73{col 65}{space 3}0.007{col 73}{space 4}-.0999783{col 86}{space 3}-.0162927
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0142835{col 45}{space 2}  .019501{col 56}{space 1}    0.73{col 65}{space 3}0.464{col 73}{space 4}-.0239957{col 86}{space 3} .0525627
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0021034{col 45}{space 2} .0195698{col 56}{space 1}    0.11{col 65}{space 3}0.914{col 73}{space 4}-.0363109{col 86}{space 3} .0405178
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0306439{col 45}{space 2} .0187776{col 56}{space 1}    1.63{col 65}{space 3}0.103{col 73}{space 4}-.0062154{col 86}{space 3} .0675031
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0214644{col 45}{space 2} .0196862{col 56}{space 1}    1.09{col 65}{space 3}0.276{col 73}{space 4}-.0171784{col 86}{space 3} .0601073
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0571049{col 45}{space 2} .0186945{col 56}{space 1}    3.05{col 65}{space 3}0.002{col 73}{space 4} .0204088{col 86}{space 3}  .093801
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2}  .416374{col 45}{space 2} .0287583{col 56}{space 1}   14.48{col 65}{space 3}0.000{col 73}{space 4} .3599233{col 86}{space 3} .4728248
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w5
{txt}
{com}. local n_baseline2 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==6, cluster(caseid)
{txt}(sum of wgt is 10,000)

Linear regression                               Number of obs     = {res}    10,000
                                                {txt}F(20, 999)        =  {res}     4.53
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0131
                                                {txt}Root MSE          =    {res} .49722

{txt}{ralign 97:(Std. Err. adjusted for {res:1,000} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0400796{col 45}{space 2} .0186356{col 56}{space 1}   -2.15{col 65}{space 3}0.032{col 73}{space 4}-.0766491{col 86}{space 3}-.0035101
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0107391{col 45}{space 2} .0188392{col 56}{space 1}    0.57{col 65}{space 3}0.569{col 73}{space 4}-.0262298{col 86}{space 3} .0477081
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0223859{col 45}{space 2} .0192625{col 56}{space 1}    1.16{col 65}{space 3}0.245{col 73}{space 4}-.0154137{col 86}{space 3} .0601856
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2}  .016131{col 45}{space 2} .0197261{col 56}{space 1}    0.82{col 65}{space 3}0.414{col 73}{space 4}-.0225784{col 86}{space 3} .0548404
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0551337{col 45}{space 2} .0190537{col 56}{space 1}    2.89{col 65}{space 3}0.004{col 73}{space 4} .0177438{col 86}{space 3} .0925235
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2}-.0008691{col 45}{space 2} .0188768{col 56}{space 1}   -0.05{col 65}{space 3}0.963{col 73}{space 4}-.0379119{col 86}{space 3} .0361738
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0282195{col 45}{space 2} .0184856{col 56}{space 1}    1.53{col 65}{space 3}0.127{col 73}{space 4}-.0080556{col 86}{space 3} .0644945
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0396358{col 45}{space 2} .0194903{col 56}{space 1}    2.03{col 65}{space 3}0.042{col 73}{space 4} .0013892{col 86}{space 3} .0778823
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0589945{col 45}{space 2} .0185949{col 56}{space 1}    3.17{col 65}{space 3}0.002{col 73}{space 4} .0225049{col 86}{space 3} .0954841
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0379783{col 45}{space 2} .0177835{col 56}{space 1}    2.14{col 65}{space 3}0.033{col 73}{space 4} .0030809{col 86}{space 3} .0728757
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0055727{col 45}{space 2} .0189023{col 56}{space 1}    0.29{col 65}{space 3}0.768{col 73}{space 4}-.0315201{col 86}{space 3} .0426655
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0211301{col 45}{space 2} .0147347{col 56}{space 1}    1.43{col 65}{space 3}0.152{col 73}{space 4}-.0077844{col 86}{space 3} .0500447
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2} .0043498{col 45}{space 2} .0153913{col 56}{space 1}    0.28{col 65}{space 3}0.778{col 73}{space 4}-.0258531{col 86}{space 3} .0345527
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0230811{col 45}{space 2} .0190582{col 56}{space 1}   -1.21{col 65}{space 3}0.226{col 73}{space 4}-.0604798{col 86}{space 3} .0143176
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0376871{col 45}{space 2} .0189886{col 56}{space 1}   -1.98{col 65}{space 3}0.047{col 73}{space 4}-.0749491{col 86}{space 3}-.0004251
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0430216{col 45}{space 2} .0190119{col 56}{space 1}    2.26{col 65}{space 3}0.024{col 73}{space 4} .0057137{col 86}{space 3} .0803294
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0177181{col 45}{space 2} .0183142{col 56}{space 1}    0.97{col 65}{space 3}0.334{col 73}{space 4}-.0182206{col 86}{space 3} .0536568
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0542733{col 45}{space 2} .0177805{col 56}{space 1}    3.05{col 65}{space 3}0.002{col 73}{space 4} .0193819{col 86}{space 3} .0891648
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0236144{col 45}{space 2} .0175248{col 56}{space 1}    1.35{col 65}{space 3}0.178{col 73}{space 4}-.0107753{col 86}{space 3}  .058004
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0730222{col 45}{space 2} .0184678{col 56}{space 1}    3.95{col 65}{space 3}0.000{col 73}{space 4} .0367821{col 86}{space 3} .1092622
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4015969{col 45}{space 2} .0271925{col 56}{space 1}   14.77{col 65}{space 3}0.000{col 73}{space 4} .3482359{col 86}{space 3} .4549579
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w6
{txt}
{com}. local n_baseline2 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==7, cluster(caseid)
{txt}(sum of wgt is 10,000)

Linear regression                               Number of obs     = {res}    10,000
                                                {txt}F(20, 999)        =  {res}     4.67
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0136
                                                {txt}Root MSE          =    {res}  .4971

{txt}{ralign 97:(Std. Err. adjusted for {res:1,000} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0488169{col 45}{space 2} .0226512{col 56}{space 1}   -2.16{col 65}{space 3}0.031{col 73}{space 4}-.0932662{col 86}{space 3}-.0043676
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2}-.0336852{col 45}{space 2} .0223681{col 56}{space 1}   -1.51{col 65}{space 3}0.132{col 73}{space 4}-.0775791{col 86}{space 3} .0102087
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0006002{col 45}{space 2} .0210475{col 56}{space 1}    0.03{col 65}{space 3}0.977{col 73}{space 4}-.0407022{col 86}{space 3} .0419027
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0636255{col 45}{space 2} .0208966{col 56}{space 1}    3.04{col 65}{space 3}0.002{col 73}{space 4} .0226193{col 86}{space 3} .1046316
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0437359{col 45}{space 2} .0187969{col 56}{space 1}    2.33{col 65}{space 3}0.020{col 73}{space 4} .0068498{col 86}{space 3} .0806219
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0296174{col 45}{space 2} .0193844{col 56}{space 1}    1.53{col 65}{space 3}0.127{col 73}{space 4}-.0084214{col 86}{space 3} .0676563
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0315715{col 45}{space 2} .0194092{col 56}{space 1}    1.63{col 65}{space 3}0.104{col 73}{space 4}-.0065159{col 86}{space 3} .0696589
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0396355{col 45}{space 2} .0194225{col 56}{space 1}    2.04{col 65}{space 3}0.042{col 73}{space 4}  .001522{col 86}{space 3} .0777489
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0364918{col 45}{space 2} .0165194{col 56}{space 1}    2.21{col 65}{space 3}0.027{col 73}{space 4} .0040751{col 86}{space 3} .0689084
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0411247{col 45}{space 2} .0175931{col 56}{space 1}    2.34{col 65}{space 3}0.020{col 73}{space 4}  .006601{col 86}{space 3} .0756485
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0166059{col 45}{space 2} .0180754{col 56}{space 1}    0.92{col 65}{space 3}0.358{col 73}{space 4}-.0188642{col 86}{space 3}  .052076
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2}-.0161752{col 45}{space 2}  .014347{col 56}{space 1}   -1.13{col 65}{space 3}0.260{col 73}{space 4}-.0443289{col 86}{space 3} .0119786
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0034453{col 45}{space 2} .0150328{col 56}{space 1}   -0.23{col 65}{space 3}0.819{col 73}{space 4}-.0329448{col 86}{space 3} .0260542
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0719125{col 45}{space 2} .0182901{col 56}{space 1}   -3.93{col 65}{space 3}0.000{col 73}{space 4}-.1078039{col 86}{space 3} -.036021
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0321912{col 45}{space 2} .0181209{col 56}{space 1}   -1.78{col 65}{space 3}0.076{col 73}{space 4}-.0677505{col 86}{space 3} .0033682
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2}-.0164318{col 45}{space 2} .0191417{col 56}{space 1}   -0.86{col 65}{space 3}0.391{col 73}{space 4}-.0539944{col 86}{space 3} .0211308
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0171266{col 45}{space 2} .0180125{col 56}{space 1}    0.95{col 65}{space 3}0.342{col 73}{space 4}-.0182201{col 86}{space 3} .0524734
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0523725{col 45}{space 2} .0171124{col 56}{space 1}    3.06{col 65}{space 3}0.002{col 73}{space 4} .0187922{col 86}{space 3} .0859528
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0091943{col 45}{space 2}  .017642{col 56}{space 1}    0.52{col 65}{space 3}0.602{col 73}{space 4}-.0254254{col 86}{space 3}  .043814
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0184653{col 45}{space 2} .0167848{col 56}{space 1}    1.10{col 65}{space 3}0.272{col 73}{space 4}-.0144722{col 86}{space 3} .0514028
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2}   .45731{col 45}{space 2} .0288708{col 56}{space 1}   15.84{col 65}{space 3}0.000{col 73}{space 4} .4006557{col 86}{space 3} .5139643
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w7
{txt}
{com}. local n_baseline2 = e(N)
{txt}
{com}. 
. coefplot ///
> (baseline_w0, label("Charlotte") msize(*1.5) msymbol(X) mcolor(black) mfcolor(black) noci) ///
> (baseline_w1, label("Cleveland") msize(*1.5) msymbol(X) mcolor(blue) mfcolor(blue) noci) ///
> (baseline_w2, label("Houston") msize(*1.5) msymbol(X) mcolor(green) mfcolor(green) noci) ///
> (baseline_w3, label("Indianapolis") msize(*1.5) msymbol(X) mcolor(red) mfcolor(red) noci) ///
> (baseline_w4, label("Memphis") msize(*1.5) msymbol(X) mcolor(magenta) mfcolor(magenta) noci) ///
> (baseline_w5, label("Rochester") msize(*1.5) msymbol(X) mcolor(sandb) mfcolor(sandb) noci) ///
> (baseline_w6, label("St. Louis") msize(*1.5) msymbol(X) mcolor(eltblue) mfcolor(eltblue) noci) ///
> (baseline_w7, label("Seattle") msize(*1.5) msymbol(X) mcolor(gs10) mfcolor(gs10) noci), ///
> legend(cols(4) region(fcolor(white) lcolor(white)) size(medium)) ///
> title(/*"Individual MSAs"*/"") xlabel(-0.15(.05) 0.15) omitted base xline(0) ///
> headings(2.educ = "{c -(}bf:Education{c )-}" 3.hieduc = "{c -(}bf:Higher Education{c )-}" 2.invest = "{c -(}bf:Investment & Taxes{c )-}" 2.gov = "{c -(}bf:Governance{c )-}" ///
> 3.workers = "{c -(}bf:Workers & Entrepreneurs{c )-}" 2.local = "{c -(}bf:Local Services{c )-}") ///
> drop(_cons 5.educ 5.hieduc 4.invest 3.gov 5.workers 4.local) ///
> order(2.invest 3.invest 1.invest 3.workers 4.workers 1.workers 2.workers 2.local 3.local 1.local 2.gov 1.gov 2.educ 4.educ 3.educ 1.educ 3.hieduc 4.hieduc 2.hieduc 1.hieduc) ///
> ylabel(, labsize(medium)) xtitle("Change in Pr(Development Plan Selected)") ytitle("") xsize(5) ysize(7) scale(.6)  
{res}{txt}
{com}. graph export "$output/fig2_b.pdf", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/fig2_b.pdf written in PDF format)

{com}. graph export "$output/fig2_b.eps", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/fig2_b.eps written in EPS format)

{com}. 
. 
. 
. 
. 
. 
. ***** Conjoint on pooled sample, by party id - strong partisans *****
. 
. *** Strong democrats
. global var "strong_democrat"
{txt}
{com}. 
. *** Load data and restrict sample
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. keep if $var==1
{txt}(6,182 observations deleted)

{com}. 
. *** Generate conjoint data
. 
. keep caseid q16*
{txt}
{com}. 
. * Number of dimensions: 6
. * Number of comparisons: 5
. * Number of plans per comparison: 2
. * Final conjoint data: 10 rows per respondent, with infomation about 6 dimensions and whether the plan was chosen
. 
. * Make 5 comparisions into rows per policy dimension
. foreach policy in educ hieduc invest gov workers local {c -(}
{txt}  2{com}. rename q16a_1_`policy' `policy'11
{txt}  3{com}. rename q16b_1_`policy' `policy'12
{txt}  4{com}. rename q16c_1_`policy' `policy'13
{txt}  5{com}. rename q16d_1_`policy' `policy'14
{txt}  6{com}. rename q16e_1_`policy' `policy'15
{txt}  7{com}. rename q16a_2_`policy' `policy'21
{txt}  8{com}. rename q16b_2_`policy' `policy'22
{txt}  9{com}. rename q16c_2_`policy' `policy'23
{txt} 10{com}. rename q16d_2_`policy' `policy'24
{txt} 11{com}. rename q16e_2_`policy' `policy'25
{txt} 12{com}. {c )-}
{res}{txt}
{com}. 
. reshape long educ1 hieduc1 invest1 gov1 workers1 local1 educ2 hieduc2 invest2 gov2 workers2 local2, i(caseid) j(table)
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1618   {txt}->{res}    8090
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}

{com}. 
. * Make 2 rows for each 5 comparisons, for each 6 policy dimensions
. sort caseid table
{txt}
{com}. g count=_n
{txt}
{com}. 
. reshape long educ hieduc invest gov workers local, i(count) j(plan)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    8090   {txt}->{res}   16180
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}

{com}. drop count
{txt}
{com}. 
. rename q16_a table1_choice
{res}{txt}
{com}. rename q16_b table2_choice
{res}{txt}
{com}. rename q16_c table3_choice
{res}{txt}
{com}. rename q16_d table4_choice
{res}{txt}
{com}. rename q16_e table5_choice
{res}{txt}
{com}. 
. g plan_chosen=.
{txt}(16,180 missing values generated)

{com}. forval t=1/5 {c -(}
{txt}  2{com}. replace plan_chosen=1 if table==`t' & plan==1 & table`t'_choice==1 
{txt}  3{com}. replace plan_chosen=0 if table==`t' & plan==1 & table`t'_choice==2
{txt}  4{com}. replace plan_chosen=1 if table==`t' & plan==2 & table`t'_choice==2 
{txt}  5{com}. replace plan_chosen=0 if table==`t' & plan==2 & table`t'_choice==1 
{txt}  6{com}. {c )-}
{txt}(835 real changes made)
(783 real changes made)
(783 real changes made)
(835 real changes made)
(839 real changes made)
(779 real changes made)
(779 real changes made)
(839 real changes made)
(838 real changes made)
(780 real changes made)
(780 real changes made)
(838 real changes made)
(830 real changes made)
(788 real changes made)
(788 real changes made)
(830 real changes made)
(855 real changes made)
(763 real changes made)
(763 real changes made)
(855 real changes made)

{com}. 
. drop table1_choice- table5_choice
{txt}
{com}. 
. 
. *** Merge with other information from the survey and save data
. 
. merge m:1 caseid using msa_survey_indiv
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,182
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,182{txt}  (_merge==2)

{col 5}matched{col 30}{res}          16,180{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. keep if $var==1
{txt}(6,182 observations deleted)

{com}. 
. save "strong_democrat_conjoint.dta", replace
{txt}file strong_democrat_conjoint.dta saved

{com}. 
. 
. 
. *** Strong republican
. global var "strong_republican"
{txt}
{com}. 
. *** Load data and restrict sample
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. keep if $var==1
{txt}(6,679 observations deleted)

{com}. 
. *** Generate conjoint data
. 
. keep caseid q16*
{txt}
{com}. 
. * Number of dimensions: 6
. * Number of comparisons: 5
. * Number of plans per comparison: 2
. * Final conjoint data: 10 rows per respondent, with infomation about 6 dimensions and whether the plan was chosen
. 
. * Make 5 comparisions into rows per policy dimension
. foreach policy in educ hieduc invest gov workers local {c -(}
{txt}  2{com}. rename q16a_1_`policy' `policy'11
{txt}  3{com}. rename q16b_1_`policy' `policy'12
{txt}  4{com}. rename q16c_1_`policy' `policy'13
{txt}  5{com}. rename q16d_1_`policy' `policy'14
{txt}  6{com}. rename q16e_1_`policy' `policy'15
{txt}  7{com}. rename q16a_2_`policy' `policy'21
{txt}  8{com}. rename q16b_2_`policy' `policy'22
{txt}  9{com}. rename q16c_2_`policy' `policy'23
{txt} 10{com}. rename q16d_2_`policy' `policy'24
{txt} 11{com}. rename q16e_2_`policy' `policy'25
{txt} 12{com}. {c )-}
{res}{txt}
{com}. 
. reshape long educ1 hieduc1 invest1 gov1 workers1 local1 educ2 hieduc2 invest2 gov2 workers2 local2, i(caseid) j(table)
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1121   {txt}->{res}    5605
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}

{com}. 
. * Make 2 rows for each 5 comparisons, for each 6 policy dimensions
. sort caseid table
{txt}
{com}. g count=_n
{txt}
{com}. 
. reshape long educ hieduc invest gov workers local, i(count) j(plan)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    5605   {txt}->{res}   11210
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}

{com}. drop count
{txt}
{com}. 
. rename q16_a table1_choice
{res}{txt}
{com}. rename q16_b table2_choice
{res}{txt}
{com}. rename q16_c table3_choice
{res}{txt}
{com}. rename q16_d table4_choice
{res}{txt}
{com}. rename q16_e table5_choice
{res}{txt}
{com}. 
. g plan_chosen=.
{txt}(11,210 missing values generated)

{com}. forval t=1/5 {c -(}
{txt}  2{com}. replace plan_chosen=1 if table==`t' & plan==1 & table`t'_choice==1 
{txt}  3{com}. replace plan_chosen=0 if table==`t' & plan==1 & table`t'_choice==2
{txt}  4{com}. replace plan_chosen=1 if table==`t' & plan==2 & table`t'_choice==2 
{txt}  5{com}. replace plan_chosen=0 if table==`t' & plan==2 & table`t'_choice==1 
{txt}  6{com}. {c )-}
{txt}(629 real changes made)
(492 real changes made)
(492 real changes made)
(629 real changes made)
(566 real changes made)
(555 real changes made)
(555 real changes made)
(566 real changes made)
(571 real changes made)
(550 real changes made)
(550 real changes made)
(571 real changes made)
(588 real changes made)
(533 real changes made)
(533 real changes made)
(588 real changes made)
(592 real changes made)
(529 real changes made)
(529 real changes made)
(592 real changes made)

{com}. 
. drop table1_choice- table5_choice
{txt}
{com}. 
. 
. *** Merge with other information from the survey and save data
. 
. merge m:1 caseid using msa_survey_indiv
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           6,679
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           6,679{txt}  (_merge==2)

{col 5}matched{col 30}{res}          11,210{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. keep if $var==1
{txt}(6,679 observations deleted)

{com}. 
. save "strong_republican_conjoint.dta", replace
{txt}file strong_republican_conjoint.dta saved

{com}. 
. 
. *** Partisan conjoints in same graph
. 
. * Strong D and strong R data
. use "strong_democrat_conjoint.dta", clear
{txt}( )

{com}. append using "strong_republican_conjoint.dta", generate(data)
{txt}(label med_trustinst2 already defined)
(label med_asc2 already defined)
(label college already defined)
(label quar14 already defined)
(label terc13Income already defined)
(label terc13 already defined)
(label med_income already defined)
(label med_trustinst already defined)
(label med_racial already defined)
(label med_asc already defined)
(label trustpeople already defined)
(label highlow already defined)
(label wf already defined)
(label single already defined)
(label children already defined)
(label gender already defined)
(label cons already defined)
(label dummy already defined)
(label hardworkthen already defined)
(label hardwork already defined)
(label temp12 already defined)
(label temp11 already defined)
(label temp10 already defined)
(label temp9 already defined)
(label temp8 already defined)
(label temp7 already defined)
(label temp6 already defined)
(label temp5 already defined)
(label temp4 already defined)
(label temp3 already defined)
(label temp2 already defined)
(label temp1 already defined)
(label trust_change already defined)
(label trust already defined)
(label trustp already defined)
(label vimp already defined)
(label satisfaction already defined)
(label immigop already defined)
(label safety already defined)
(label ineqsmall already defined)
(label agree already defined)
(label Q66 already defined)
(label RACIALD already defined)
(label RACIALC already defined)
(label RACIALB already defined)
(label RACIALA already defined)
(label Q62 already defined)
(label Q61 already defined)
(label Q60 already defined)
(label Q59 already defined)
(label Q57 already defined)
(label Q56 already defined)
(label Q55_8_GE already defined)
(label Q55_7_GE already defined)
(label Q55_6_GE already defined)
(label Q55_5_GE already defined)
(label Q55_4_GE already defined)
(label Q55_3_GE already defined)
(label Q55_2_GE already defined)
(label Q55_1_GE already defined)
(label Q54 already defined)
(label Q53 already defined)
(label Q52 already defined)
(label Q49 already defined)
(label Q48 already defined)
(label Q46_OTHE already defined)
(label Q46 already defined)
(label Q43 already defined)
(label Q42 already defined)
(label Q41_B already defined)
(label Q41_A already defined)
(label Q41 already defined)
(label Q37 already defined)
(label Q36G already defined)
(label Q36F already defined)
(label Q36E already defined)
(label Q36D already defined)
(label Q36C already defined)
(label Q36B already defined)
(label Q36A already defined)
(label Q31R already defined)
(label Q31Q already defined)
(label Q31P already defined)
(label Q31O already defined)
(label Q31N already defined)
(label Q31M already defined)
(label Q31L already defined)
(label Q31K already defined)
(label Q31J already defined)
(label Q31I already defined)
(label Q31H already defined)
(label Q31G already defined)
(label Q31F already defined)
(label Q31E already defined)
(label Q31D already defined)
(label Q31C already defined)
(label Q31B already defined)
(label Q31A already defined)
(label Q30 already defined)
(label Q29 already defined)
(label V120_A already defined)
(label V119_A already defined)
(label V118_A already defined)
(label V117_A already defined)
(label V116_A already defined)
(label Q22_TEST already defined)
(label Q20 already defined)
(label Q19 already defined)
(label Q16_E already defined)
(label Q16_D already defined)
(label Q16_C already defined)
(label Q16_B already defined)
(label Q16_A already defined)
(label Q16E_2_L already defined)
(label Q16E_1_L already defined)
(label Q16E_2_W already defined)
(label Q16E_1_W already defined)
(label Q16E_2_G already defined)
(label Q16E_1_G already defined)
(label Q16E_2_I already defined)
(label Q16E_1_I already defined)
(label Q16E_2_H already defined)
(label Q16E_1_H already defined)
(label Q16E_2_E already defined)
(label Q16E_1_E already defined)
(label Q16D_2_L already defined)
(label Q16D_1_L already defined)
(label Q16D_2_W already defined)
(label Q16D_1_W already defined)
(label Q16D_2_G already defined)
(label Q16D_1_G already defined)
(label Q16D_2_I already defined)
(label Q16D_1_I already defined)
(label Q16D_2_H already defined)
(label Q16D_1_H already defined)
(label Q16D_2_E already defined)
(label Q16D_1_E already defined)
(label Q16C_2_L already defined)
(label Q16C_1_L already defined)
(label Q16C_2_W already defined)
(label Q16C_1_W already defined)
(label Q16C_2_G already defined)
(label Q16C_1_G already defined)
(label Q16C_2_I already defined)
(label Q16C_1_I already defined)
(label Q16C_2_H already defined)
(label Q16C_1_H already defined)
(label Q16C_2_E already defined)
(label Q16C_1_E already defined)
(label Q16B_2_L already defined)
(label Q16B_1_L already defined)
(label Q16B_2_W already defined)
(label Q16B_1_W already defined)
(label Q16B_2_G already defined)
(label Q16B_1_G already defined)
(label Q16B_2_I already defined)
(label Q16B_1_I already defined)
(label Q16B_2_H already defined)
(label Q16B_1_H already defined)
(label Q16B_2_E already defined)
(label Q16B_1_E already defined)
(label Q16A_2_L already defined)
(label Q16A_1_L already defined)
(label Q16A_2_W already defined)
(label Q16A_1_W already defined)
(label Q16A_2_G already defined)
(label Q16A_1_G already defined)
(label Q16A_2_I already defined)
(label Q16A_1_I already defined)
(label Q16A_2_H already defined)
(label Q16A_1_H already defined)
(label Q16A_2_E already defined)
(label Q16A_1_E already defined)
(label Q15 already defined)
(label Q14 already defined)
(label Q11 already defined)
(label Q10 already defined)
(label Q8 already defined)
(label Q6 already defined)
(label Q3_ALL already defined)
(label Q2_ALL already defined)
(label Q1_ALL already defined)
(label Q50 already defined)
(label Q58 already defined)
(label MSA already defined)
(label INPUTSTA already defined)
(label Q44 already defined)

{com}. set scheme s1mono // Graph layout
{txt}
{com}. 
. * Short value labels
. lab def educ 1 "Charter schools" 2 "Vouchers to schools" 3 "Free pre-school" 4 "Pay teachers more" 5 "Keep current" 
{txt}
{com}. lab def hieduc 1 "Community colleges" 2 "Local public universities" 3 "Technical vocational training" 4 "Student grant programs" 5 "Keep current" 
{txt}
{com}. lab def invest 1 "Attract new businesses" 2 "Stimulate existing companies" 3 "Encourage investment charities" 4 "Keep current" 
{txt}
{com}. lab def gov 1 "Consolidate local government" 2 "More power to the state" 3 "Keep current"
{txt}
{com}. lab def workers 1 "Limit unions' power" 2 "Expand unions' power" 3 "Worker training vouchers" 4 "Tax breaks to entrepreneurs" 5 "Keep current"
{txt}
{com}. lab def local 1 "Affordable housing" 2 "Public transportation" 3 "Safety and crime prevention" 4 "Keep current"   
{txt}
{com}. lab values educ educ
{txt}
{com}. lab values hieduc hieduc
{txt}
{com}. lab values invest invest
{txt}
{com}. lab values gov gov
{txt}
{com}. lab values workers workers
{txt}
{com}. lab values local local
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==0, cluster(caseid)
{txt}(sum of wgt is 16,839.2045409739)

Linear regression                               Number of obs     = {res}    16,180
                                                {txt}F(20, 1617)       =  {res}     8.47
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0206
                                                {txt}Root MSE          =    {res} .49514

{txt}{ralign 97:(Std. Err. adjusted for {res:1,618} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0450884{col 45}{space 2} .0161469{col 56}{space 1}   -2.79{col 65}{space 3}0.005{col 73}{space 4}-.0767594{col 86}{space 3}-.0134174
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2}-.0381262{col 45}{space 2} .0166889{col 56}{space 1}   -2.28{col 65}{space 3}0.022{col 73}{space 4}-.0708603{col 86}{space 3}-.0053921
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} .0524119{col 45}{space 2} .0158746{col 56}{space 1}    3.30{col 65}{space 3}0.001{col 73}{space 4} .0212749{col 86}{space 3}  .083549
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2}  .075387{col 45}{space 2}  .015824{col 56}{space 1}    4.76{col 65}{space 3}0.000{col 73}{space 4} .0443493{col 86}{space 3} .1064248
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0264617{col 45}{space 2} .0165749{col 56}{space 1}    1.60{col 65}{space 3}0.111{col 73}{space 4}-.0060489{col 86}{space 3} .0589723
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0088563{col 45}{space 2} .0157178{col 56}{space 1}    0.56{col 65}{space 3}0.573{col 73}{space 4} -.021973{col 86}{space 3} .0396857
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0400955{col 45}{space 2} .0159223{col 56}{space 1}    2.52{col 65}{space 3}0.012{col 73}{space 4} .0088649{col 86}{space 3}  .071326
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0636447{col 45}{space 2} .0169777{col 56}{space 1}    3.75{col 65}{space 3}0.000{col 73}{space 4}  .030344{col 86}{space 3} .0969454
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0289499{col 45}{space 2} .0135919{col 56}{space 1}    2.13{col 65}{space 3}0.033{col 73}{space 4} .0022904{col 86}{space 3} .0556094
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0221864{col 45}{space 2} .0143539{col 56}{space 1}    1.55{col 65}{space 3}0.122{col 73}{space 4}-.0059677{col 86}{space 3} .0503406
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0133458{col 45}{space 2} .0150599{col 56}{space 1}    0.89{col 65}{space 3}0.376{col 73}{space 4}-.0161932{col 86}{space 3} .0428849
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0162986{col 45}{space 2} .0127923{col 56}{space 1}    1.27{col 65}{space 3}0.203{col 73}{space 4}-.0087926{col 86}{space 3} .0413898
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0051973{col 45}{space 2} .0130324{col 56}{space 1}   -0.40{col 65}{space 3}0.690{col 73}{space 4}-.0307596{col 86}{space 3}  .020365
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0782062{col 45}{space 2} .0166205{col 56}{space 1}   -4.71{col 65}{space 3}0.000{col 73}{space 4}-.1108061{col 86}{space 3}-.0456063
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2} .0103207{col 45}{space 2} .0154802{col 56}{space 1}    0.67{col 65}{space 3}0.505{col 73}{space 4}-.0200427{col 86}{space 3} .0406841
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0227133{col 45}{space 2} .0158564{col 56}{space 1}    1.43{col 65}{space 3}0.152{col 73}{space 4} -.008388{col 86}{space 3} .0538146
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0361211{col 45}{space 2}  .016091{col 56}{space 1}    2.24{col 65}{space 3}0.025{col 73}{space 4} .0045596{col 86}{space 3} .0676825
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0621165{col 45}{space 2}  .014565{col 56}{space 1}    4.26{col 65}{space 3}0.000{col 73}{space 4} .0335481{col 86}{space 3} .0906848
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2} .0195876{col 45}{space 2} .0145619{col 56}{space 1}    1.35{col 65}{space 3}0.179{col 73}{space 4}-.0089746{col 86}{space 3} .0481498
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0147954{col 45}{space 2} .0147964{col 56}{space 1}    1.00{col 65}{space 3}0.317{col 73}{space 4}-.0142267{col 86}{space 3} .0438175
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4204664{col 45}{space 2} .0234681{col 56}{space 1}   17.92{col 65}{space 3}0.000{col 73}{space 4} .3744353{col 86}{space 3} .4664976
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w0
{txt}
{com}. local n_baseline0 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==1, cluster(caseid)
{txt}(sum of wgt is 11,495.0809689651)

Linear regression                               Number of obs     = {res}    11,210
                                                {txt}F(20, 1120)       =  {res}     8.65
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0229
                                                {txt}Root MSE          =    {res}  .4947

{txt}{ralign 97:(Std. Err. adjusted for {res:1,121} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2} .0456137{col 45}{space 2} .0206727{col 56}{space 1}    2.21{col 65}{space 3}0.028{col 73}{space 4} .0050523{col 86}{space 3} .0861752
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0552234{col 45}{space 2} .0187726{col 56}{space 1}    2.94{col 65}{space 3}0.003{col 73}{space 4}   .01839{col 86}{space 3} .0920568
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2} -.019942{col 45}{space 2} .0175166{col 56}{space 1}   -1.14{col 65}{space 3}0.255{col 73}{space 4}-.0543111{col 86}{space 3}  .014427
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0359411{col 45}{space 2} .0175349{col 56}{space 1}    2.05{col 65}{space 3}0.041{col 73}{space 4} .0015361{col 86}{space 3} .0703461
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0349283{col 45}{space 2} .0176058{col 56}{space 1}    1.98{col 65}{space 3}0.048{col 73}{space 4} .0003843{col 86}{space 3} .0694723
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2}-.0019922{col 45}{space 2} .0176894{col 56}{space 1}   -0.11{col 65}{space 3}0.910{col 73}{space 4}-.0367003{col 86}{space 3}  .032716
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0337047{col 45}{space 2} .0183923{col 56}{space 1}    1.83{col 65}{space 3}0.067{col 73}{space 4}-.0023826{col 86}{space 3}  .069792
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0075888{col 45}{space 2} .0179727{col 56}{space 1}    0.42{col 65}{space 3}0.673{col 73}{space 4}-.0276753{col 86}{space 3} .0428528
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0569138{col 45}{space 2} .0153436{col 56}{space 1}    3.71{col 65}{space 3}0.000{col 73}{space 4} .0268083{col 86}{space 3} .0870193
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0563328{col 45}{space 2} .0160848{col 56}{space 1}    3.50{col 65}{space 3}0.000{col 73}{space 4} .0247731{col 86}{space 3} .0878925
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2}-.0023304{col 45}{space 2} .0145926{col 56}{space 1}   -0.16{col 65}{space 3}0.873{col 73}{space 4}-.0309624{col 86}{space 3} .0263016
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2} .0117185{col 45}{space 2}  .014767{col 56}{space 1}    0.79{col 65}{space 3}0.428{col 73}{space 4}-.0172555{col 86}{space 3} .0406926
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2} -.001124{col 45}{space 2} .0140675{col 56}{space 1}   -0.08{col 65}{space 3}0.936{col 73}{space 4}-.0287256{col 86}{space 3} .0264777
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2} -.026115{col 45}{space 2} .0182309{col 56}{space 1}   -1.43{col 65}{space 3}0.152{col 73}{space 4}-.0618855{col 86}{space 3} .0096555
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.1318199{col 45}{space 2} .0184375{col 56}{space 1}   -7.15{col 65}{space 3}0.000{col 73}{space 4}-.1679958{col 86}{space 3}-.0956439
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2}-.0251325{col 45}{space 2} .0180375{col 56}{space 1}   -1.39{col 65}{space 3}0.164{col 73}{space 4}-.0605235{col 86}{space 3} .0102586
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0183384{col 45}{space 2} .0171238{col 56}{space 1}    1.07{col 65}{space 3}0.284{col 73}{space 4}-.0152599{col 86}{space 3} .0519367
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0111983{col 45}{space 2} .0169202{col 56}{space 1}    0.66{col 65}{space 3}0.508{col 73}{space 4}-.0220005{col 86}{space 3} .0443972
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2}-.0162315{col 45}{space 2} .0164474{col 56}{space 1}   -0.99{col 65}{space 3}0.324{col 73}{space 4}-.0485027{col 86}{space 3} .0160397
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2}  .072482{col 45}{space 2} .0144821{col 56}{space 1}    5.00{col 65}{space 3}0.000{col 73}{space 4} .0440669{col 86}{space 3}  .100897
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4462335{col 45}{space 2} .0257498{col 56}{space 1}   17.33{col 65}{space 3}0.000{col 73}{space 4} .3957103{col 86}{space 3} .4967568
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w1
{txt}
{com}. local n_baseline1 = e(N)
{txt}
{com}. 
. coefplot ///
> (baseline_w0, label("Strong Democrats") msymbol(circle) mcolor(black) mfcolor(none) ciopts(lcolor(black) lwidth(thin))) ///
> (baseline_w1, label("Strong Republicans") msymbol(circle) mcolor(black) ciopts(lcolor(black) lwidth(thin))), ///
> legend(cols(3) region(fcolor(white) lcolor(white)) size(medium)) ///
> title(/*"Strong Democrats and Republicans"*/"") xlabel(-0.15(.05) 0.15) omitted base xline(0) ///
> headings(2.educ = "{c -(}bf:Education{c )-}" 3.hieduc = "{c -(}bf:Higher Education{c )-}" 2.invest = "{c -(}bf:Investment & Taxes{c )-}" 2.gov = "{c -(}bf:Governance{c )-}" ///
> 3.workers = "{c -(}bf:Workers & Entrepreneurs{c )-}" 2.local = "{c -(}bf:Local Services{c )-}") ///
> drop(_cons 5.educ 5.hieduc 4.invest 3.gov 5.workers 4.local) ///
> order(2.invest 3.invest 1.invest 3.workers 4.workers 1.workers 2.workers 2.local 3.local 1.local 2.gov 1.gov 2.educ 4.educ 3.educ 1.educ 3.hieduc 4.hieduc 2.hieduc 1.hieduc) ///
> ylabel(, labsize(medium)) xtitle("Change in Pr(Development Plan Selected)") ytitle("") xsize(5) ysize(7) scale(.6)  
{res}{txt}
{com}. graph export "$output/fig3.pdf", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/fig3.pdf written in PDF format)

{com}. graph export "$output/fig3.eps", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/fig3.eps written in EPS format)

{com}. 
. 
. 
. 
. ***** Conjoint on pooled sample, by party id - standard question ("Do you think of yourself as a...")  *****
. 
. *** Democrat
. global var "democrat"
{txt}
{com}. 
. *** Load data and restrict sample
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. keep if $var==1
{txt}(5,059 observations deleted)

{com}. 
. *** Generate conjoint data
. 
. keep caseid q16*
{txt}
{com}. 
. * Number of dimensions: 6
. * Number of comparisons: 5
. * Number of plans per comparison: 2
. * Final conjoint data: 10 rows per respondent, with infomation about 6 dimensions and whether the plan was chosen
. 
. * Make 5 comparisions into rows per policy dimension
. foreach policy in educ hieduc invest gov workers local {c -(}
{txt}  2{com}. rename q16a_1_`policy' `policy'11
{txt}  3{com}. rename q16b_1_`policy' `policy'12
{txt}  4{com}. rename q16c_1_`policy' `policy'13
{txt}  5{com}. rename q16d_1_`policy' `policy'14
{txt}  6{com}. rename q16e_1_`policy' `policy'15
{txt}  7{com}. rename q16a_2_`policy' `policy'21
{txt}  8{com}. rename q16b_2_`policy' `policy'22
{txt}  9{com}. rename q16c_2_`policy' `policy'23
{txt} 10{com}. rename q16d_2_`policy' `policy'24
{txt} 11{com}. rename q16e_2_`policy' `policy'25
{txt} 12{com}. {c )-}
{res}{txt}
{com}. 
. reshape long educ1 hieduc1 invest1 gov1 workers1 local1 educ2 hieduc2 invest2 gov2 workers2 local2, i(caseid) j(table)
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    2741   {txt}->{res}   13705
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}

{com}. 
. * Make 2 rows for each 5 comparisons, for each 6 policy dimensions
. sort caseid table
{txt}
{com}. g count=_n
{txt}
{com}. 
. reshape long educ hieduc invest gov workers local, i(count) j(plan)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}   13705   {txt}->{res}   27410
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}

{com}. drop count
{txt}
{com}. 
. rename q16_a table1_choice
{res}{txt}
{com}. rename q16_b table2_choice
{res}{txt}
{com}. rename q16_c table3_choice
{res}{txt}
{com}. rename q16_d table4_choice
{res}{txt}
{com}. rename q16_e table5_choice
{res}{txt}
{com}. 
. g plan_chosen=.
{txt}(27,410 missing values generated)

{com}. forval t=1/5 {c -(}
{txt}  2{com}. replace plan_chosen=1 if table==`t' & plan==1 & table`t'_choice==1 
{txt}  3{com}. replace plan_chosen=0 if table==`t' & plan==1 & table`t'_choice==2
{txt}  4{com}. replace plan_chosen=1 if table==`t' & plan==2 & table`t'_choice==2 
{txt}  5{com}. replace plan_chosen=0 if table==`t' & plan==2 & table`t'_choice==1 
{txt}  6{com}. {c )-}
{txt}(1,430 real changes made)
(1,311 real changes made)
(1,311 real changes made)
(1,430 real changes made)
(1,417 real changes made)
(1,324 real changes made)
(1,324 real changes made)
(1,417 real changes made)
(1,427 real changes made)
(1,314 real changes made)
(1,314 real changes made)
(1,427 real changes made)
(1,429 real changes made)
(1,312 real changes made)
(1,312 real changes made)
(1,429 real changes made)
(1,452 real changes made)
(1,289 real changes made)
(1,289 real changes made)
(1,452 real changes made)

{com}. 
. drop table1_choice- table5_choice
{txt}
{com}. 
. 
. *** Merge with other information from the survey and save data
. 
. merge m:1 caseid using msa_survey_indiv
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           5,059
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           5,059{txt}  (_merge==2)

{col 5}matched{col 30}{res}          27,410{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. keep if $var==1
{txt}(5,059 observations deleted)

{com}. 
. save "democrat_conjoint.dta", replace
{txt}file democrat_conjoint.dta saved

{com}. 
. 
. *** Republican
. global var "republican"
{txt}
{com}. 
. *** Load data and restrict sample
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. keep if $var==1
{txt}(5,763 observations deleted)

{com}. 
. *** Generate conjoint data
. 
. keep caseid q16*
{txt}
{com}. 
. * Number of dimensions: 6
. * Number of comparisons: 5
. * Number of plans per comparison: 2
. * Final conjoint data: 10 rows per respondent, with infomation about 6 dimensions and whether the plan was chosen
. 
. * Make 5 comparisions into rows per policy dimension
. foreach policy in educ hieduc invest gov workers local {c -(}
{txt}  2{com}. rename q16a_1_`policy' `policy'11
{txt}  3{com}. rename q16b_1_`policy' `policy'12
{txt}  4{com}. rename q16c_1_`policy' `policy'13
{txt}  5{com}. rename q16d_1_`policy' `policy'14
{txt}  6{com}. rename q16e_1_`policy' `policy'15
{txt}  7{com}. rename q16a_2_`policy' `policy'21
{txt}  8{com}. rename q16b_2_`policy' `policy'22
{txt}  9{com}. rename q16c_2_`policy' `policy'23
{txt} 10{com}. rename q16d_2_`policy' `policy'24
{txt} 11{com}. rename q16e_2_`policy' `policy'25
{txt} 12{com}. {c )-}
{res}{txt}
{com}. 
. reshape long educ1 hieduc1 invest1 gov1 workers1 local1 educ2 hieduc2 invest2 gov2 workers2 local2, i(caseid) j(table)
{txt}(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    2037   {txt}->{res}   10185
{txt}Number of variables            {res}      66   {txt}->{res}      19
{txt}j variable (5 values)                     ->   {res}table
{txt}xij variables:
               {res}educ11 educ12 ... educ15   {txt}->   {res}educ1
         hieduc11 hieduc12 ... hieduc15   {txt}->   {res}hieduc1
         invest11 invest12 ... invest15   {txt}->   {res}invest1
                  gov11 gov12 ... gov15   {txt}->   {res}gov1
      workers11 workers12 ... workers15   {txt}->   {res}workers1
            local11 local12 ... local15   {txt}->   {res}local1
               educ21 educ22 ... educ25   {txt}->   {res}educ2
         hieduc21 hieduc22 ... hieduc25   {txt}->   {res}hieduc2
         invest21 invest22 ... invest25   {txt}->   {res}invest2
                  gov21 gov22 ... gov25   {txt}->   {res}gov2
      workers21 workers22 ... workers25   {txt}->   {res}workers2
            local21 local22 ... local25   {txt}->   {res}local2
{txt}{hline 77}

{com}. 
. * Make 2 rows for each 5 comparisons, for each 6 policy dimensions
. sort caseid table
{txt}
{com}. g count=_n
{txt}
{com}. 
. reshape long educ hieduc invest gov workers local, i(count) j(plan)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}   10185   {txt}->{res}   20370
{txt}Number of variables            {res}      20   {txt}->{res}      15
{txt}j variable (2 values)                     ->   {res}plan
{txt}xij variables:
                            {res}educ1 educ2   {txt}->   {res}educ
                        hieduc1 hieduc2   {txt}->   {res}hieduc
                        invest1 invest2   {txt}->   {res}invest
                              gov1 gov2   {txt}->   {res}gov
                      workers1 workers2   {txt}->   {res}workers
                          local1 local2   {txt}->   {res}local
{txt}{hline 77}

{com}. drop count
{txt}
{com}. 
. rename q16_a table1_choice
{res}{txt}
{com}. rename q16_b table2_choice
{res}{txt}
{com}. rename q16_c table3_choice
{res}{txt}
{com}. rename q16_d table4_choice
{res}{txt}
{com}. rename q16_e table5_choice
{res}{txt}
{com}. 
. g plan_chosen=.
{txt}(20,370 missing values generated)

{com}. forval t=1/5 {c -(}
{txt}  2{com}. replace plan_chosen=1 if table==`t' & plan==1 & table`t'_choice==1 
{txt}  3{com}. replace plan_chosen=0 if table==`t' & plan==1 & table`t'_choice==2
{txt}  4{com}. replace plan_chosen=1 if table==`t' & plan==2 & table`t'_choice==2 
{txt}  5{com}. replace plan_chosen=0 if table==`t' & plan==2 & table`t'_choice==1 
{txt}  6{com}. {c )-}
{txt}(1,115 real changes made)
(922 real changes made)
(922 real changes made)
(1,115 real changes made)
(1,035 real changes made)
(1,002 real changes made)
(1,002 real changes made)
(1,035 real changes made)
(1,044 real changes made)
(993 real changes made)
(993 real changes made)
(1,044 real changes made)
(1,100 real changes made)
(937 real changes made)
(937 real changes made)
(1,100 real changes made)
(1,068 real changes made)
(969 real changes made)
(969 real changes made)
(1,068 real changes made)

{com}. 
. drop table1_choice- table5_choice
{txt}
{com}. 
. 
. *** Merge with other information from the survey and save data
. 
. merge m:1 caseid using msa_survey_indiv
{res}{txt}(label Q44 already defined)
(label INPUTSTA already defined)
(label MSA already defined)
(label Q58 already defined)
(label Q50 already defined)
(label Q51 already defined)
(label Q1_ALL already defined)
(label Q2_ALL already defined)
(label Q3_ALL already defined)
(label Q6 already defined)
(label Q8 already defined)
(label Q10 already defined)
(label Q11 already defined)
(label Q14 already defined)
(label Q15 already defined)
(label Q16A_1_E already defined)
(label Q16A_2_E already defined)
(label Q16A_1_H already defined)
(label Q16A_2_H already defined)
(label Q16A_1_I already defined)
(label Q16A_2_I already defined)
(label Q16A_1_G already defined)
(label Q16A_2_G already defined)
(label Q16A_1_W already defined)
(label Q16A_2_W already defined)
(label Q16A_1_L already defined)
(label Q16A_2_L already defined)
(label Q16B_1_E already defined)
(label Q16B_2_E already defined)
(label Q16B_1_H already defined)
(label Q16B_2_H already defined)
(label Q16B_1_I already defined)
(label Q16B_2_I already defined)
(label Q16B_1_G already defined)
(label Q16B_2_G already defined)
(label Q16B_1_W already defined)
(label Q16B_2_W already defined)
(label Q16B_1_L already defined)
(label Q16B_2_L already defined)
(label Q16C_1_E already defined)
(label Q16C_2_E already defined)
(label Q16C_1_H already defined)
(label Q16C_2_H already defined)
(label Q16C_1_I already defined)
(label Q16C_2_I already defined)
(label Q16C_1_G already defined)
(label Q16C_2_G already defined)
(label Q16C_1_W already defined)
(label Q16C_2_W already defined)
(label Q16C_1_L already defined)
(label Q16C_2_L already defined)
(label Q16D_1_E already defined)
(label Q16D_2_E already defined)
(label Q16D_1_H already defined)
(label Q16D_2_H already defined)
(label Q16D_1_I already defined)
(label Q16D_2_I already defined)
(label Q16D_1_G already defined)
(label Q16D_2_G already defined)
(label Q16D_1_W already defined)
(label Q16D_2_W already defined)
(label Q16D_1_L already defined)
(label Q16D_2_L already defined)
(label Q16E_1_E already defined)
(label Q16E_2_E already defined)
(label Q16E_1_H already defined)
(label Q16E_2_H already defined)
(label Q16E_1_I already defined)
(label Q16E_2_I already defined)
(label Q16E_1_G already defined)
(label Q16E_2_G already defined)
(label Q16E_1_W already defined)
(label Q16E_2_W already defined)
(label Q16E_1_L already defined)
(label Q16E_2_L already defined)
(label Q16_A already defined)
(label Q16_B already defined)
(label Q16_C already defined)
(label Q16_D already defined)
(label Q16_E already defined)
(label Q19 already defined)
(label Q20 already defined)
(label Q22_TEST already defined)
(label V116_A already defined)
(label V117_A already defined)
(label V118_A already defined)
(label V119_A already defined)
(label V120_A already defined)
(label Q29 already defined)
(label Q30 already defined)
(label Q31A already defined)
(label Q31B already defined)
(label Q31C already defined)
(label Q31D already defined)
(label Q31E already defined)
(label Q31F already defined)
(label Q31G already defined)
(label Q31H already defined)
(label Q31I already defined)
(label Q31J already defined)
(label Q31K already defined)
(label Q31L already defined)
(label Q31M already defined)
(label Q31N already defined)
(label Q31O already defined)
(label Q31P already defined)
(label Q31Q already defined)
(label Q31R already defined)
(label Q36A already defined)
(label Q36B already defined)
(label Q36C already defined)
(label Q36D already defined)
(label Q36E already defined)
(label Q36F already defined)
(label Q36G already defined)
(label Q37 already defined)
(label Q41 already defined)
(label Q41_A already defined)
(label Q41_B already defined)
(label Q42 already defined)
(label Q43 already defined)
(label Q46 already defined)
(label Q46_OTHE already defined)
(label Q48 already defined)
(label Q49 already defined)
(label Q52 already defined)
(label Q53 already defined)
(label Q54 already defined)
(label Q55_1_GE already defined)
(label Q55_2_GE already defined)
(label Q55_3_GE already defined)
(label Q55_4_GE already defined)
(label Q55_5_GE already defined)
(label Q55_6_GE already defined)
(label Q55_7_GE already defined)
(label Q55_8_GE already defined)
(label Q56 already defined)
(label Q57 already defined)
(label Q59 already defined)
(label Q60 already defined)
(label Q61 already defined)
(label Q62 already defined)
(label RACIALA already defined)
(label RACIALB already defined)
(label RACIALC already defined)
(label RACIALD already defined)
(label Q66 already defined)
(label agree already defined)
(label ineqsmall already defined)
(label safety already defined)
(label immigop already defined)
(label satisfaction already defined)
(label vimp already defined)
(label trustp already defined)
(label trust already defined)
(label trust_change already defined)
(label temp1 already defined)
(label temp2 already defined)
(label temp3 already defined)
(label temp4 already defined)
(label temp5 already defined)
(label temp6 already defined)
(label temp7 already defined)
(label temp8 already defined)
(label temp9 already defined)
(label temp10 already defined)
(label temp11 already defined)
(label temp12 already defined)
(label hardwork already defined)
(label hardworkthen already defined)
(label dummy already defined)
(label cons already defined)
(label gender already defined)
(label children already defined)
(label single already defined)
(label wf already defined)
(label highlow already defined)
(label trustpeople already defined)
(label med_asc already defined)
(label med_racial already defined)
(label med_trustinst already defined)
(label med_income already defined)
(label terc13 already defined)
(label terc13Income already defined)
(label quar14 already defined)
(label college already defined)
(label med_asc2 already defined)
(label med_trustinst2 already defined)

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           5,763
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           5,763{txt}  (_merge==2)

{col 5}matched{col 30}{res}          20,370{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. keep if $var==1
{txt}(5,763 observations deleted)

{com}. 
. save "republican_conjoint.dta", replace
{txt}file republican_conjoint.dta saved

{com}. 
. 
. 
. *** Partisan conjoints in same graph
. 
. * D and R data
. use "democrat_conjoint.dta", clear
{txt}( )

{com}. append using "republican_conjoint.dta", generate(data)
{txt}(label med_trustinst2 already defined)
(label med_asc2 already defined)
(label college already defined)
(label quar14 already defined)
(label terc13Income already defined)
(label terc13 already defined)
(label med_income already defined)
(label med_trustinst already defined)
(label med_racial already defined)
(label med_asc already defined)
(label trustpeople already defined)
(label highlow already defined)
(label wf already defined)
(label single already defined)
(label children already defined)
(label gender already defined)
(label cons already defined)
(label dummy already defined)
(label hardworkthen already defined)
(label hardwork already defined)
(label temp12 already defined)
(label temp11 already defined)
(label temp10 already defined)
(label temp9 already defined)
(label temp8 already defined)
(label temp7 already defined)
(label temp6 already defined)
(label temp5 already defined)
(label temp4 already defined)
(label temp3 already defined)
(label temp2 already defined)
(label temp1 already defined)
(label trust_change already defined)
(label trust already defined)
(label trustp already defined)
(label vimp already defined)
(label satisfaction already defined)
(label immigop already defined)
(label safety already defined)
(label ineqsmall already defined)
(label agree already defined)
(label Q66 already defined)
(label RACIALD already defined)
(label RACIALC already defined)
(label RACIALB already defined)
(label RACIALA already defined)
(label Q62 already defined)
(label Q61 already defined)
(label Q60 already defined)
(label Q59 already defined)
(label Q57 already defined)
(label Q56 already defined)
(label Q55_8_GE already defined)
(label Q55_7_GE already defined)
(label Q55_6_GE already defined)
(label Q55_5_GE already defined)
(label Q55_4_GE already defined)
(label Q55_3_GE already defined)
(label Q55_2_GE already defined)
(label Q55_1_GE already defined)
(label Q54 already defined)
(label Q53 already defined)
(label Q52 already defined)
(label Q49 already defined)
(label Q48 already defined)
(label Q46_OTHE already defined)
(label Q46 already defined)
(label Q43 already defined)
(label Q42 already defined)
(label Q41_B already defined)
(label Q41_A already defined)
(label Q41 already defined)
(label Q37 already defined)
(label Q36G already defined)
(label Q36F already defined)
(label Q36E already defined)
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(label Q36C already defined)
(label Q36B already defined)
(label Q36A already defined)
(label Q31R already defined)
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(label Q31P already defined)
(label Q31O already defined)
(label Q31N already defined)
(label Q31M already defined)
(label Q31L already defined)
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(label Q31H already defined)
(label Q31G already defined)
(label Q31F already defined)
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(label Q31D already defined)
(label Q31C already defined)
(label Q31B already defined)
(label Q31A already defined)
(label Q30 already defined)
(label Q29 already defined)
(label V120_A already defined)
(label V119_A already defined)
(label V118_A already defined)
(label V117_A already defined)
(label V116_A already defined)
(label Q22_TEST already defined)
(label Q20 already defined)
(label Q19 already defined)
(label Q16_E already defined)
(label Q16_D already defined)
(label Q16_C already defined)
(label Q16_B already defined)
(label Q16_A already defined)
(label Q16E_2_L already defined)
(label Q16E_1_L already defined)
(label Q16E_2_W already defined)
(label Q16E_1_W already defined)
(label Q16E_2_G already defined)
(label Q16E_1_G already defined)
(label Q16E_2_I already defined)
(label Q16E_1_I already defined)
(label Q16E_2_H already defined)
(label Q16E_1_H already defined)
(label Q16E_2_E already defined)
(label Q16E_1_E already defined)
(label Q16D_2_L already defined)
(label Q16D_1_L already defined)
(label Q16D_2_W already defined)
(label Q16D_1_W already defined)
(label Q16D_2_G already defined)
(label Q16D_1_G already defined)
(label Q16D_2_I already defined)
(label Q16D_1_I already defined)
(label Q16D_2_H already defined)
(label Q16D_1_H already defined)
(label Q16D_2_E already defined)
(label Q16D_1_E already defined)
(label Q16C_2_L already defined)
(label Q16C_1_L already defined)
(label Q16C_2_W already defined)
(label Q16C_1_W already defined)
(label Q16C_2_G already defined)
(label Q16C_1_G already defined)
(label Q16C_2_I already defined)
(label Q16C_1_I already defined)
(label Q16C_2_H already defined)
(label Q16C_1_H already defined)
(label Q16C_2_E already defined)
(label Q16C_1_E already defined)
(label Q16B_2_L already defined)
(label Q16B_1_L already defined)
(label Q16B_2_W already defined)
(label Q16B_1_W already defined)
(label Q16B_2_G already defined)
(label Q16B_1_G already defined)
(label Q16B_2_I already defined)
(label Q16B_1_I already defined)
(label Q16B_2_H already defined)
(label Q16B_1_H already defined)
(label Q16B_2_E already defined)
(label Q16B_1_E already defined)
(label Q16A_2_L already defined)
(label Q16A_1_L already defined)
(label Q16A_2_W already defined)
(label Q16A_1_W already defined)
(label Q16A_2_G already defined)
(label Q16A_1_G already defined)
(label Q16A_2_I already defined)
(label Q16A_1_I already defined)
(label Q16A_2_H already defined)
(label Q16A_1_H already defined)
(label Q16A_2_E already defined)
(label Q16A_1_E already defined)
(label Q15 already defined)
(label Q14 already defined)
(label Q11 already defined)
(label Q10 already defined)
(label Q8 already defined)
(label Q6 already defined)
(label Q3_ALL already defined)
(label Q2_ALL already defined)
(label Q1_ALL already defined)
(label Q50 already defined)
(label Q58 already defined)
(label MSA already defined)
(label INPUTSTA already defined)
(label Q44 already defined)

{com}. set scheme s1mono // Graph layout
{txt}
{com}. 
. * Short value labels
. lab def educ 1 "Charter schools" 2 "Vouchers to schools" 3 "Free pre-school" 4 "Pay teachers more" 5 "Keep current" 
{txt}
{com}. lab def hieduc 1 "Community colleges" 2 "Local public universities" 3 "Technical vocational training" 4 "Student grant programs" 5 "Keep current" 
{txt}
{com}. lab def invest 1 "Attract new businesses" 2 "Stimulate existing companies" 3 "Encourage investment charities" 4 "Keep current" 
{txt}
{com}. lab def gov 1 "Consolidate local government" 2 "More power to the state" 3 "Keep current"
{txt}
{com}. lab def workers 1 "Limit unions' power" 2 "Expand unions' power" 3 "Worker training vouchers" 4 "Tax breaks to entrepreneurs" 5 "Keep current"
{txt}
{com}. lab def local 1 "Affordable housing" 2 "Public transportation" 3 "Safety and crime prevention" 4 "Keep current"   
{txt}
{com}. lab values educ educ
{txt}
{com}. lab values hieduc hieduc
{txt}
{com}. lab values invest invest
{txt}
{com}. lab values gov gov
{txt}
{com}. lab values workers workers
{txt}
{com}. lab values local local
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==0, cluster(caseid)
{txt}(sum of wgt is 28,129.6703775191)

Linear regression                               Number of obs     = {res}    27,410
                                                {txt}F(20, 2740)       =  {res}    12.75
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0179
                                                {txt}Root MSE          =    {res} .49569

{txt}{ralign 97:(Std. Err. adjusted for {res:2,741} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}-.0464307{col 45}{space 2}  .012172{col 56}{space 1}   -3.81{col 65}{space 3}0.000{col 73}{space 4}-.0702978{col 86}{space 3}-.0225636
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2}-.0277702{col 45}{space 2} .0135316{col 56}{space 1}   -2.05{col 65}{space 3}0.040{col 73}{space 4}-.0543033{col 86}{space 3} -.001237
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2}  .042744{col 45}{space 2}   .01227{col 56}{space 1}    3.48{col 65}{space 3}0.001{col 73}{space 4} .0186845{col 86}{space 3} .0668034
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2}  .070659{col 45}{space 2} .0124933{col 56}{space 1}    5.66{col 65}{space 3}0.000{col 73}{space 4} .0461618{col 86}{space 3} .0951563
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2}  .039953{col 45}{space 2} .0123638{col 56}{space 1}    3.23{col 65}{space 3}0.001{col 73}{space 4} .0157096{col 86}{space 3} .0641963
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0331699{col 45}{space 2} .0122189{col 56}{space 1}    2.71{col 65}{space 3}0.007{col 73}{space 4} .0092107{col 86}{space 3} .0571291
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0380522{col 45}{space 2} .0120473{col 56}{space 1}    3.16{col 65}{space 3}0.002{col 73}{space 4} .0144295{col 86}{space 3} .0616749
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0612424{col 45}{space 2} .0124522{col 56}{space 1}    4.92{col 65}{space 3}0.000{col 73}{space 4} .0368257{col 86}{space 3} .0856591
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2}  .040204{col 45}{space 2} .0113805{col 56}{space 1}    3.53{col 65}{space 3}0.000{col 73}{space 4} .0178888{col 86}{space 3} .0625193
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2}   .03386{col 45}{space 2}  .011428{col 56}{space 1}    2.96{col 65}{space 3}0.003{col 73}{space 4} .0114516{col 86}{space 3} .0562683
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0122017{col 45}{space 2} .0125878{col 56}{space 1}    0.97{col 65}{space 3}0.332{col 73}{space 4}-.0124808{col 86}{space 3} .0368843
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2}  .015062{col 45}{space 2} .0097807{col 56}{space 1}    1.54{col 65}{space 3}0.124{col 73}{space 4}-.0041163{col 86}{space 3} .0342402
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}  .003958{col 45}{space 2} .0099311{col 56}{space 1}    0.40{col 65}{space 3}0.690{col 73}{space 4}-.0155152{col 86}{space 3} .0234312
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0759189{col 45}{space 2} .0126524{col 56}{space 1}   -6.00{col 65}{space 3}0.000{col 73}{space 4}-.1007282{col 86}{space 3}-.0511096
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.0072566{col 45}{space 2} .0119659{col 56}{space 1}   -0.61{col 65}{space 3}0.544{col 73}{space 4}-.0307196{col 86}{space 3} .0162064
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2} .0075294{col 45}{space 2} .0121776{col 56}{space 1}    0.62{col 65}{space 3}0.536{col 73}{space 4}-.0163488{col 86}{space 3} .0314077
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0330286{col 45}{space 2} .0123012{col 56}{space 1}    2.68{col 65}{space 3}0.007{col 73}{space 4}  .008908{col 86}{space 3} .0571493
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0657192{col 45}{space 2} .0108385{col 56}{space 1}    6.06{col 65}{space 3}0.000{col 73}{space 4} .0444667{col 86}{space 3} .0869717
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2}  .024943{col 45}{space 2} .0114216{col 56}{space 1}    2.18{col 65}{space 3}0.029{col 73}{space 4} .0025471{col 86}{space 3} .0473388
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0372611{col 45}{space 2} .0116213{col 56}{space 1}    3.21{col 65}{space 3}0.001{col 73}{space 4} .0144736{col 86}{space 3} .0600486
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4060169{col 45}{space 2} .0178761{col 56}{space 1}   22.71{col 65}{space 3}0.000{col 73}{space 4}  .370965{col 86}{space 3} .4410688
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w0
{txt}
{com}. local n_baseline0 = e(N)
{txt}
{com}. 
. reg plan_chosen ib5.educ ib5.hieduc ib4.invest ib3.gov ib5.workers ib4.local [pweight=weight] if data==1, cluster(caseid)
{txt}(sum of wgt is 20,535.8843574631)

Linear regression                               Number of obs     = {res}    20,370
                                                {txt}F(20, 2036)       =  {res}    12.10
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0186
                                                {txt}Root MSE          =    {res} .49559

{txt}{ralign 97:(Std. Err. adjusted for {res:2,037} clusters in caseid)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                    plan_chosen{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}educ {c |}
{space 15}Charter schools  {c |}{col 33}{res}{space 2}  .014716{col 45}{space 2} .0142179{col 56}{space 1}    1.04{col 65}{space 3}0.301{col 73}{space 4}-.0131671{col 86}{space 3} .0425991
{txt}{space 11}Vouchers to schools  {c |}{col 33}{res}{space 2} .0407657{col 45}{space 2} .0138189{col 56}{space 1}    2.95{col 65}{space 3}0.003{col 73}{space 4} .0136649{col 86}{space 3} .0678664
{txt}{space 15}Free pre-school  {c |}{col 33}{res}{space 2}-.0134023{col 45}{space 2} .0132512{col 56}{space 1}   -1.01{col 65}{space 3}0.312{col 73}{space 4}-.0393895{col 86}{space 3}  .012585
{txt}{space 13}Pay teachers more  {c |}{col 33}{res}{space 2} .0379323{col 45}{space 2} .0135949{col 56}{space 1}    2.79{col 65}{space 3}0.005{col 73}{space 4} .0112708{col 86}{space 3} .0645937
{txt}{space 31} {c |}
{space 25}hieduc {c |}
{space 12}Community colleges  {c |}{col 33}{res}{space 2} .0388314{col 45}{space 2} .0135484{col 56}{space 1}    2.87{col 65}{space 3}0.004{col 73}{space 4} .0122614{col 86}{space 3} .0654015
{txt}{space 5}Local public universities  {c |}{col 33}{res}{space 2} .0114682{col 45}{space 2} .0140371{col 56}{space 1}    0.82{col 65}{space 3}0.414{col 73}{space 4}-.0160604{col 86}{space 3} .0389967
{txt}{space 1}Technical vocational training  {c |}{col 33}{res}{space 2} .0495542{col 45}{space 2}  .013557{col 56}{space 1}    3.66{col 65}{space 3}0.000{col 73}{space 4} .0229672{col 86}{space 3} .0761412
{txt}{space 8}Student grant programs  {c |}{col 33}{res}{space 2} .0136134{col 45}{space 2}   .01325{col 56}{space 1}    1.03{col 65}{space 3}0.304{col 73}{space 4}-.0123716{col 86}{space 3} .0395983
{txt}{space 31} {c |}
{space 25}invest {c |}
{space 8}Attract new businesses  {c |}{col 33}{res}{space 2} .0652709{col 45}{space 2} .0116631{col 56}{space 1}    5.60{col 65}{space 3}0.000{col 73}{space 4}  .042398{col 86}{space 3} .0881438
{txt}{space 2}Stimulate existing companies  {c |}{col 33}{res}{space 2} .0594923{col 45}{space 2} .0121836{col 56}{space 1}    4.88{col 65}{space 3}0.000{col 73}{space 4} .0355986{col 86}{space 3}  .083386
{txt}Encourage investment charities  {c |}{col 33}{res}{space 2} .0252882{col 45}{space 2} .0118272{col 56}{space 1}    2.14{col 65}{space 3}0.033{col 73}{space 4} .0020936{col 86}{space 3} .0484828
{txt}{space 31} {c |}
{space 28}gov {c |}
{space 2}Consolidate local government  {c |}{col 33}{res}{space 2}-.0034612{col 45}{space 2} .0111518{col 56}{space 1}   -0.31{col 65}{space 3}0.756{col 73}{space 4}-.0253313{col 86}{space 3}  .018409
{txt}{space 7}More power to the state  {c |}{col 33}{res}{space 2}-.0045708{col 45}{space 2} .0106001{col 56}{space 1}   -0.43{col 65}{space 3}0.666{col 73}{space 4}-.0253589{col 86}{space 3} .0162174
{txt}{space 31} {c |}
{space 24}workers {c |}
{space 11}Limit unions' power  {c |}{col 33}{res}{space 2}-.0172334{col 45}{space 2}  .013583{col 56}{space 1}   -1.27{col 65}{space 3}0.205{col 73}{space 4}-.0438715{col 86}{space 3} .0094046
{txt}{space 10}Expand unions' power  {c |}{col 33}{res}{space 2}-.1022243{col 45}{space 2} .0139645{col 56}{space 1}   -7.32{col 65}{space 3}0.000{col 73}{space 4}-.1296105{col 86}{space 3}-.0748381
{txt}{space 6}Worker training vouchers  {c |}{col 33}{res}{space 2}-.0088786{col 45}{space 2} .0132672{col 56}{space 1}   -0.67{col 65}{space 3}0.503{col 73}{space 4}-.0348973{col 86}{space 3}   .01714
{txt}{space 3}Tax breaks to entrepreneurs  {c |}{col 33}{res}{space 2} .0218726{col 45}{space 2} .0125534{col 56}{space 1}    1.74{col 65}{space 3}0.082{col 73}{space 4}-.0027463{col 86}{space 3} .0464915
{txt}{space 31} {c |}
{space 26}local {c |}
{space 12}Affordable housing  {c |}{col 33}{res}{space 2} .0142787{col 45}{space 2} .0125988{col 56}{space 1}    1.13{col 65}{space 3}0.257{col 73}{space 4}-.0104291{col 86}{space 3} .0389866
{txt}{space 9}Public transportation  {c |}{col 33}{res}{space 2}-.0207764{col 45}{space 2} .0118556{col 56}{space 1}   -1.75{col 65}{space 3}0.080{col 73}{space 4}-.0440268{col 86}{space 3} .0024739
{txt}{space 3}Safety and crime prevention  {c |}{col 33}{res}{space 2} .0775783{col 45}{space 2} .0117476{col 56}{space 1}    6.60{col 65}{space 3}0.000{col 73}{space 4} .0545398{col 86}{space 3} .1006168
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .4298824{col 45}{space 2} .0191494{col 56}{space 1}   22.45{col 65}{space 3}0.000{col 73}{space 4} .3923279{col 86}{space 3}  .467437
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimate store baseline_w1
{txt}
{com}. local n_baseline1 = e(N)
{txt}
{com}. 
. coefplot ///
> (baseline_w0, label("Democrats") msymbol(circle) mcolor(black) mfcolor(none) ciopts(lcolor(black) lwidth(thin))) ///
> (baseline_w1, label("Republicans") msymbol(circle) mcolor(black) ciopts(lcolor(black) lwidth(thin))), ///
> legend(cols(3) region(fcolor(white) lcolor(white)) size(medium)) ///
> title(""/*"Democrats and Republicans"*/) xlabel(-0.15(.05) 0.15) omitted base xline(0) ///
> headings(2.educ = "{c -(}bf:Education{c )-}" 3.hieduc = "{c -(}bf:Higher Education{c )-}" 2.invest = "{c -(}bf:Investment & Taxes{c )-}" 2.gov = "{c -(}bf:Governance{c )-}" ///
> 3.workers = "{c -(}bf:Workers & Entrepreneurs{c )-}" 2.local = "{c -(}bf:Local Services{c )-}") ///
> drop(_cons 5.educ 5.hieduc 4.invest 3.gov 5.workers 4.local) ///
> order(2.invest 3.invest 1.invest 3.workers 4.workers 1.workers 2.workers 2.local 3.local 1.local 2.gov 1.gov 2.educ 4.educ 3.educ 1.educ 3.hieduc 4.hieduc 2.hieduc 1.hieduc) ///
> ylabel(, labsize(medium)) xtitle("Change in Pr(Development Plan Selected)") ytitle("") xsize(5) ysize(7) scale(.6)  
{res}{txt}
{com}. graph export "$output/figA4.pdf", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA4.pdf written in PDF format)

{com}. graph export "$output/figA4.eps", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA4.eps written in EPS format)

{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. 
. 
. 
. *** Code to run policy importance bar graphs (Appendix Figure A-1, A-2, A-3)
. do "$localdir/Code/policy_importance_analysis.do"
{txt}
{com}. 
. *********** This program generates Figure A-1, A-2, and A-3 of the appendix ***********
. 
. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. set scheme s1mono
{txt}
{com}. 
. cd "$localdir/Data"
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Data
{txt}
{com}. gl output "$localdir/Output"
{txt}
{com}. 
. 
. *** Load analysis data
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. * MSA evaluation
. g msa_got_worse=(changes_msa_worse==4 | changes_msa_worse==5) if changes_msa_worse!=.
{txt}
{com}. lab var msa_got_worse "MSA Economy Got Worse"
{txt}
{com}. 
. * Save data used to generate bar graphs data
. save local_national_data, replace
{txt}file local_national_data.dta saved

{com}. 
. 
. 
. *** Generate bar graphs data
. 
. use local_national_data, clear
{txt}( )

{com}. 
. g total=_N
{txt}
{com}. bysort imp_perf_factor_localecpolicy: g local_total=_N
{txt}
{com}. g localshare=local_total/total
{txt}
{com}. 
. g scale=1 if imp_perf_factor_localecpolicy==1
{txt}(7,563 missing values generated)

{com}. replace scale=2 if imp_perf_factor_localecpolicy==2
{txt}(884 real changes made)

{com}. replace scale=3 if imp_perf_factor_localecpolicy==3
{txt}(3,261 real changes made)

{com}. replace scale=4 if imp_perf_factor_localecpolicy==4
{txt}(3,418 real changes made)

{com}. 
. keep scale localshare
{txt}
{com}. duplicates drop scale,force

{p 0 4}{txt}Duplicates in terms of {res} scale{p_end}

{txt}(7,796 observations deleted)

{com}. 
. save local, replace
{txt}file local.dta saved

{com}. 
. 
. use local_national_data, clear
{txt}( )

{com}. keep if msa_got_worse==0
{txt}(2,889 observations deleted)

{com}. 
. g total=_N
{txt}
{com}. bysort imp_perf_factor_localecpolicy: g local_total=_N
{txt}
{com}. g localshare_better=local_total/total
{txt}
{com}. 
. g scale=1 if imp_perf_factor_localecpolicy==1
{txt}(4,728 missing values generated)

{com}. replace scale=2 if imp_perf_factor_localecpolicy==2
{txt}(674 real changes made)

{com}. replace scale=3 if imp_perf_factor_localecpolicy==3
{txt}(2,264 real changes made)

{com}. replace scale=4 if imp_perf_factor_localecpolicy==4
{txt}(1,790 real changes made)

{com}. 
. keep scale localshare_better
{txt}
{com}. duplicates drop scale,force

{p 0 4}{txt}Duplicates in terms of {res} scale{p_end}

{txt}(4,907 observations deleted)

{com}. 
. save local_better, replace
{txt}file local_better.dta saved

{com}. 
. 
. use local_national_data, clear
{txt}( )

{com}. keep if msa_got_worse==1
{txt}(4,911 observations deleted)

{com}. 
. g total=_N
{txt}
{com}. bysort imp_perf_factor_localecpolicy: g local_total=_N
{txt}
{com}. g localshare_worse=local_total/total
{txt}
{com}. 
. g scale=1 if imp_perf_factor_localecpolicy==1
{txt}(2,835 missing values generated)

{com}. replace scale=2 if imp_perf_factor_localecpolicy==2
{txt}(210 real changes made)

{com}. replace scale=3 if imp_perf_factor_localecpolicy==3
{txt}(997 real changes made)

{com}. replace scale=4 if imp_perf_factor_localecpolicy==4
{txt}(1,628 real changes made)

{com}. 
. keep scale localshare_worse
{txt}
{com}. duplicates drop scale,force

{p 0 4}{txt}Duplicates in terms of {res} scale{p_end}

{txt}(2,885 observations deleted)

{com}. 
. save local_worse, replace
{txt}file local_worse.dta saved

{com}. 
. 
. use local_national_data, clear
{txt}( )

{com}. 
. g total=_N
{txt}
{com}. bysort imp_perf_factor_nationalecpolicy: g national_total=_N
{txt}
{com}. g nationalshare=national_total/total
{txt}
{com}. 
. g scale=1 if imp_perf_factor_nationalecpolicy==1
{txt}(7,465 missing values generated)

{com}. replace scale=2 if imp_perf_factor_nationalecpolicy==2
{txt}(1,322 real changes made)

{com}. replace scale=3 if imp_perf_factor_nationalecpolicy==3
{txt}(3,332 real changes made)

{com}. replace scale=4 if imp_perf_factor_nationalecpolicy==4
{txt}(2,811 real changes made)

{com}. 
. keep scale nationalshare
{txt}
{com}. duplicates drop scale, force

{p 0 4}{txt}Duplicates in terms of {res} scale{p_end}

{txt}(7,796 observations deleted)

{com}. 
. save national, replace
{txt}file national.dta saved

{com}. 
. 
. use local_national_data, clear
{txt}( )

{com}. keep if msa_got_worse==0
{txt}(2,889 observations deleted)

{com}. 
. g total=_N
{txt}
{com}. bysort imp_perf_factor_nationalecpolicy: g national_total=_N
{txt}
{com}. g nationalshare_better=national_total/total
{txt}
{com}. 
. g scale=1 if imp_perf_factor_nationalecpolicy==1
{txt}(4,662 missing values generated)

{com}. replace scale=2 if imp_perf_factor_nationalecpolicy==2
{txt}(987 real changes made)

{com}. replace scale=3 if imp_perf_factor_nationalecpolicy==3
{txt}(2,233 real changes made)

{com}. replace scale=4 if imp_perf_factor_nationalecpolicy==4
{txt}(1,442 real changes made)

{com}. 
. keep scale nationalshare_better
{txt}
{com}. duplicates drop scale, force

{p 0 4}{txt}Duplicates in terms of {res} scale{p_end}

{txt}(4,907 observations deleted)

{com}. 
. save national_better, replace
{txt}file national_better.dta saved

{com}. 
. 
. use local_national_data, clear
{txt}( )

{com}. keep if msa_got_worse==1
{txt}(4,911 observations deleted)

{com}. 
. g total=_N
{txt}
{com}. bysort imp_perf_factor_nationalecpolicy: g national_total=_N
{txt}
{com}. g nationalshare_worse=national_total/total
{txt}
{com}. 
. g scale=1 if imp_perf_factor_nationalecpolicy==1
{txt}(2,803 missing values generated)

{com}. replace scale=2 if imp_perf_factor_nationalecpolicy==2
{txt}(335 real changes made)

{com}. replace scale=3 if imp_perf_factor_nationalecpolicy==3
{txt}(1,099 real changes made)

{com}. replace scale=4 if imp_perf_factor_nationalecpolicy==4
{txt}(1,369 real changes made)

{com}. 
. keep scale nationalshare_worse
{txt}
{com}. duplicates drop scale, force

{p 0 4}{txt}Duplicates in terms of {res} scale{p_end}

{txt}(2,885 observations deleted)

{com}. 
. save national_worse, replace
{txt}file national_worse.dta saved

{com}. 
. 
. use national, clear
{txt}( )

{com}. merge 1:1 scale using national_better
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}               4{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. merge 1:1 scale using national_worse
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}               4{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. merge 1:1 scale using local
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}               4{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. merge 1:1 scale using local_better
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}               4{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. merge 1:1 scale using local_worse
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}               4{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. 
. lab def scale 1 "Not Important" 2 "Of Little Importance" 3 "Somewhat Important" 4 "Very Important"
{txt}
{com}. lab val scale scale
{txt}
{com}. 
. foreach x of varlist nationalshare localshare {c -(}
{txt}  2{com}. replace `x'=`x'*100
{txt}  3{com}. replace `x'_better=`x'_better*100
{txt}  4{com}. replace `x'_worse=`x'_worse*100
{txt}  5{com}. {c )-}
{txt}(4 real changes made)
(4 real changes made)
(4 real changes made)
(4 real changes made)
(4 real changes made)
(4 real changes made)

{com}. 
. 
. *** Generate bar graphs
. 
. graph bar nationalshare localshare, over(scale, lab(labsize(small)) gap(80)) ///
> bar(1, color(midblue)) bar(2, color(ltblue)) bargap(0) ///
> legend(lab(1 "National Economic Policies") lab(2 "Local Economic Policies") ///
> region(fcolor(white) lcolor(white)) size(small) cols(2) symx(*0.5) symy(*0.5) keyg(*0.8) colg(5)) graphregion(color(white) margin(l=5)) ///
> title("", size(medium) margin(b+3)) ///
> yscale(titlegap(*5)) ytitle("Respondents (%)") ylabel(0 (20) 60) 
{res}{txt}
{com}. 
. graph export "$output/figA1.pdf", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA1.pdf written in PDF format)

{com}. graph export "$output/figA1.eps", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA1.eps written in EPS format)

{com}. 
. 
. graph bar nationalshare_better localshare_better, over(scale, lab(labsize(small)) gap(80)) ///
> bar(1, color(midblue)) bar(2, color(ltblue)) bargap(0) ///
> legend(lab(1 "National Economic Policies") lab(2 "Local Economic Policies") ///
> region(fcolor(white) lcolor(white)) size(small) cols(2) symx(*0.5) symy(*0.5) keyg(*0.8) colg(5)) graphregion(color(white) margin(l=5)) ///
> title("", size(medium) margin(b+3)) ///
> yscale(titlegap(*5)) ytitle("Respondents (%)") ylabel(0 (20) 60) 
{res}{txt}
{com}. 
. graph export "$output/figA2.pdf", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA2.pdf written in PDF format)

{com}. graph export "$output/figA2.eps", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA2.eps written in EPS format)

{com}. 
. 
. graph bar nationalshare_worse localshare_worse, over(scale, lab(labsize(small)) gap(80)) ///
> bar(1, color(midblue)) bar(2, color(ltblue)) bargap(0) ///
> legend(lab(1 "National Economic Policies") lab(2 "Local Economic Policies") ///
> region(fcolor(white) lcolor(white)) size(small) cols(2) symx(*0.5) symy(*0.5) keyg(*0.8) colg(5)) graphregion(color(white) margin(l=5)) ///
> title("", size(medium) margin(b+3)) ///
> yscale(titlegap(*5)) ytitle("Respondents (%)") ylabel(0 (20) 60) 
{res}{txt}
{com}. 
. graph export "$output/figA3.pdf", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA3.pdf written in PDF format)

{com}. graph export "$output/figA3.eps", replace
{txt}(file /Users/wpmarble/Dropbox/Cities/Publication_Files/Output/figA3.eps written in EPS format)

{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. 
. 
. 
. 
. ******* Code to run word statistics (Table A-2, A-3, A-4) *******
. * First file generates the text data. 
. do "$localdir/Code/generate_text_data.do"
{txt}
{com}. /*
>  This program creates txt files based on the raw data used as input in R to do 
>  the text analysis.
>  
>  Note that there are a few manual clean-ups of the txt files -- described at 
>  the end of this program -- implemented before running the R code.
>  
>  Data from R is loaded back into Stata in pt. 2 of this program to generate 
>  respondent-level word analyses and word tables (Table A-2, A-3, A-4 of the appendix) 
> */
. 
. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. cd "$localdir/Data"
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Data
{txt}
{com}. 
. *** Read survey data and save a text file with the relevant question
. * Question q63: Issues facing MSA
. 
. use "STAN0107_OUTPUT_8msas.dta", clear
{txt}( )

{com}. g count=_n
{txt}
{com}. keep Q63 
{txt}
{com}. export delimited "q63.txt", replace
{res}{txt}file q63.txt saved

{com}. 
. 
. *** Manually clean text the txt file before running the R code
. * The "'" symbol - for instance used in "don't" - show up as "í" or "ì" or "î". Correct this with "find and replace" in the text file.
. * There is one "ñ" in the text, this is replaced with "and" in the text file. 
. * There is one sentence with three "ó" symbols, these are replaced with blank space " ". 
. * There is one "·" in a Spanish sentence, this is deleted.
. 
. *** A new txt file is saved as _clean.txt after the clean-up
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. 
. // NOTE: Now, implement a few manual edits to txt file from "generate_text_data.do", 
. // described in the do-file, then run R code "word_count.R". Then run the next line.
. 
. * Run R file to count words (can also do outside of stata)
. cd $localdir
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files
{txt}
{com}. shell $rexec --vanilla <"$localdir/Code/word_count.R"
{txt}

R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 
> # install.packages("foreign")
> # install.packages("tm")
> # install.packages("SnowballC")
> # install.packages("wordcloud")
> # install.packages("NLP")
> # install.packages("RColorBrewer")
> # install.packages("SnowballC")
> 
> library(foreign)
> library(tm)
Loading required package: NLP
> library(SnowballC)
Warning message:
package ‘SnowballC’ was built under R version 3.6.2 
> library(wordcloud)
Loading required package: RColorBrewer
> library(RColorBrewer)
> 
> # rm(list=ls())
> # setwd("/Users/amaliejensen/Dropbox/Cities/AJ_files/PSRM Final/Data")
> 
> getwd()
[1] "/Users/wpmarble/Dropbox/Cities/Publication_Files"
> 
> ### Pooled data
> data <- read.delim('Data/q63_clean.txt')
> subdataCorpus <- Corpus(VectorSource(data$Q63))
> # If you want to check a specific line:subdataCorpus[["168"]][["content"]]
> subdataCorpus <- tm_map(subdataCorpus, content_transformer(tolower))
Warning message:
In tm_map.SimpleCorpus(subdataCorpus, content_transformer(tolower)) :
  transformation drops documents
> subdataCorpus <- tm_map(subdataCorpus, removePunctuation)
Warning message:
In tm_map.SimpleCorpus(subdataCorpus, removePunctuation) :
  transformation drops documents
> subdataCorpus <- tm_map(subdataCorpus, removeWords, stopwords('english'))
Warning message:
In tm_map.SimpleCorpus(subdataCorpus, removeWords, stopwords("english")) :
  transformation drops documents
> 
> # Word table dataset
> dtm <- TermDocumentMatrix(subdataCorpus)
> m <- as.matrix(dtm)
> v <- sort(rowSums(m),decreasing=TRUE)
> d <- data.frame(word = names(v),freq=v)
> head(d, 10)
                     word freq
crime               crime 1644
jobs                 jobs 1044
housing           housing  762
people             people  690
education       education  636
lack                 lack  635
poverty           poverty  506
high                 high  397
homelessness homelessness  387
government     government  358
> write.dta(d ,"Data/pooled_wordcloud_q63.dta")
> 
> 
> 
> 
> 
> 
> 

{com}. 
. 
. * run final text analysis script, which makes Table A-2, A-3, and A-4 
. do "$localdir/Code/text_analysis.do"
{txt}
{com}. 
. ********** This program generates results shown in Table A-2, A-3, and A-4 of the appendix **********
. ***** The program uses word tables generated in R as input *****
. 
. /*
> In do-file "generate_text_data" we read the YouGov dataset, keep the relevant 
> open ended text variable, and save a txt file with the information in the variable.
> 
> In R program "word_count" we read the txt file, clean the text data, make a word 
> count and construct a word table dataset. 
> 
> In this do-file, we use the most popular words (from the R word table dataset) 
> to analyze use of these words at the individual respondent level.
> */
. 
. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. cd "$localdir/Data"
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Data
{txt}
{com}. gl output "$localdir/Output"
{txt}
{com}. 
. 
. ***** Generate words data and statistics using word counts from R in pooled (all MSAs) sample *****
. 
. *** Find most popular words based on word count table from R
. * Look at words mentioned at least 100 times in pooled sample, pick 20 most mentioned nouns
. 
. * Load word count pooled data
. use pooled_wordcloud_q63, replace
{txt}(Written by R.              )

{com}. 
. gsort -freq
{txt}
{com}. *br
. 
. * 20 most popular nouns 
. /*
> crime
> job
> housing
> people
> education
> lack
> poverty
> homeless
> government
> transport
> violence
> city
> public
> unemployment
> traffic
> issue
> living
> tax
> drug
> cost
> */
. 
. 
. 
. *** Indicators for whether respondent mentions a word at least once 
. 
. * Load survey data
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. keep major_issues_msa_text caseid msa
{txt}
{com}. * Check strings
. g count=_n
{txt}
{com}. fre major_issues_msa_text if count==161, width(100)
{res}
{txt}major_issues_msa_text {hline 2} Major issues facing people in the MSA
{txt}{hline 54}{hline 1}{c TT}{hline 44}
{txt}        {txt}                                               {c |}      Freq.    Percent      Valid       Cum.
{txt}{hline 54}{hline 1}{c +}{hline 44}
{txt}Valid   The dangers of Donald Trump and the Republican {c |}{res}          1     100.00     100.00     100.00
{txt}        party. Loss of social safety nets. Lack of     {c |}{res}                                            
{txt}        affordable housing, transportation and         {c |}{res}                                            
{txt}        infrastructure problems. Loss of natural       {c |}{res}                                            
{txt}        spaces due to overdevopment in order to        {c |}{res}                                            
{txt}        accomodate people moving into the area.        {c |}{res}                                            
{txt}{hline 54}{hline 1}{c BT}{hline 44}

{com}. fre major_issues_msa_text if count==1094, width(100)
{res}
{txt}major_issues_msa_text {hline 2} Major issues facing people in the MSA
{txt}{hline 18}{hline 1}{c TT}{hline 44}
{txt}        {txt}           {c |}      Freq.    Percent      Valid       Cum.
{txt}{hline 18}{hline 1}{c +}{hline 44}
{txt}Valid   Don�t know {c |}{res}          1     100.00     100.00     100.00
{txt}{hline 18}{hline 1}{c BT}{hline 44}

{com}. drop count
{txt}
{com}. * Note: some of the words contain weird symbols (for instance "Don�t"), as the "'" symbol is not transferred correctly to Stata. 
. * However, this does not matter for the word counts (as none of the words we look at have "'" in them).
. * This was fixed for the text files used in R to determine most used words.
. 
. gen temp=strpos(major_issues_msa_text, "crime") | strpos(major_issues_msa_text, "Crime")
{txt}
{com}. gen crime=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "job") | strpos(major_issues_msa_text, "Job")
{txt}
{com}. gen job=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "housing") | strpos(major_issues_msa_text, "Housing")
{txt}
{com}. gen housing=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "people") | strpos(major_issues_msa_text, "People")
{txt}
{com}. gen people=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "education") | strpos(major_issues_msa_text, "Education")
{txt}
{com}. gen education=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "lack") | strpos(major_issues_msa_text, "Lack")
{txt}
{com}. gen lack=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "poverty") | strpos(major_issues_msa_text, "Poverty")
{txt}
{com}. gen poverty=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "homeless") | strpos(major_issues_msa_text, "Homeless")
{txt}
{com}. gen homeless=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "government") | strpos(major_issues_msa_text, "Government")
{txt}
{com}. gen government=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "transport") | strpos(major_issues_msa_text, "Transport")
{txt}
{com}. gen transport=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "violence") | strpos(major_issues_msa_text, "Violence")
{txt}
{com}. gen violence=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "city") | strpos(major_issues_msa_text, "City")
{txt}
{com}. gen city=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "public") | strpos(major_issues_msa_text, "Public")
{txt}
{com}. gen public=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "unemployment") | strpos(major_issues_msa_text, "Unemployment")
{txt}
{com}. gen unemployment=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "traffic") | strpos(major_issues_msa_text, "Traffic")
{txt}
{com}. gen traffic=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "issue") | strpos(major_issues_msa_text, "Issue")
{txt}
{com}. gen issue=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "living") | strpos(major_issues_msa_text, "Living")
{txt}
{com}. gen living=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "tax") | strpos(major_issues_msa_text, "Tax")
{txt}
{com}. gen tax=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "drug") | strpos(major_issues_msa_text, "Drug")
{txt}
{com}. gen drug=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "cost") | strpos(major_issues_msa_text, "Cost")
{txt}
{com}. gen cost=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. * Save data with individual indicators for mentioning a word
. save single_words, replace
{txt}file single_words.dta saved

{com}. 
. 
. 
. *** Indicators for whether respondent mentions a word from a word category at least once 
. 
. * Load survey data
. use msa_survey_indiv, clear
{txt}( )

{com}. 
. * Construct categories based on words that appear at least 100 times in pooled sample
. * Categories:
. * Crime: crime, violence, drugs, police, safety
. * Economy and employment: job, work, income, money, wage, unemployment, employment, economy, economic, growth, paying
. * Housing: housing, affordable, living, price
. * Education: education, schools, 
. * Poverty and social issues: poverty, poor, inequality, health, homeless
. * Traffic and transport: infrastructure, transport, traffic, 
. * Government and politics: government, public, city, taxes, area, system, local, people, issues, state
. * Race: racial, racism, race
. 
. keep major_issues_msa_text caseid msa
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "crime") | strpos(major_issues_msa_text, "Crime") | ///
> strpos(major_issues_msa_text, "violence") | strpos(major_issues_msa_text, "Violence") | ///
> strpos(major_issues_msa_text, "drug") | strpos(major_issues_msa_text, "Drug") | ///
> strpos(major_issues_msa_text, "police") | strpos(major_issues_msa_text, "Police") | ///
> strpos(major_issues_msa_text, "safety") | strpos(major_issues_msa_text, "Safety")
{txt}
{com}. gen crime_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "job") | strpos(major_issues_msa_text, "Job") | ///
> strpos(major_issues_msa_text, "work") | strpos(major_issues_msa_text, "Work") | ///
> strpos(major_issues_msa_text, "income") | strpos(major_issues_msa_text, "Income") | ///
> strpos(major_issues_msa_text, "money") | strpos(major_issues_msa_text, "Money") | ///
> strpos(major_issues_msa_text, "wage") | strpos(major_issues_msa_text, "Wage") | ///
> strpos(major_issues_msa_text, "unemployment") | strpos(major_issues_msa_text, "Unemployment") | ///
> strpos(major_issues_msa_text, "employment") | strpos(major_issues_msa_text, "Employment") | ///
> strpos(major_issues_msa_text, "economy") | strpos(major_issues_msa_text, "Economy") | ///
> strpos(major_issues_msa_text, "economic") | strpos(major_issues_msa_text, "Economic") | ///
> strpos(major_issues_msa_text, "growth") | strpos(major_issues_msa_text, "Growth") | ///
> strpos(major_issues_msa_text, "paying") | strpos(major_issues_msa_text, "Paying")
{txt}
{com}. gen economy_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "housing") | strpos(major_issues_msa_text, "Housing") | ///
> strpos(major_issues_msa_text, "affordable") | strpos(major_issues_msa_text, "Affordable") | ///
> strpos(major_issues_msa_text, "living") | strpos(major_issues_msa_text, "Living") | ///
> strpos(major_issues_msa_text, "price") | strpos(major_issues_msa_text, "Price") 
{txt}
{com}. gen housing_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "education") | strpos(major_issues_msa_text, "Edcuation") | ///
> strpos(major_issues_msa_text, "school") | strpos(major_issues_msa_text, "School") 
{txt}
{com}. gen education_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "poverty") | strpos(major_issues_msa_text, "Poverty") | ///
> strpos(major_issues_msa_text, "poor") | strpos(major_issues_msa_text, "Poor") | ///
> strpos(major_issues_msa_text, "inequality") | strpos(major_issues_msa_text, "Inequality") | ///
> strpos(major_issues_msa_text, "health") | strpos(major_issues_msa_text, "Health") | ///
> strpos(major_issues_msa_text, "homeless") | strpos(major_issues_msa_text, "Homeless")
{txt}
{com}. gen social_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "infrastructure") | strpos(major_issues_msa_text, "Infrastructure") | ///
> strpos(major_issues_msa_text, "transport") | strpos(major_issues_msa_text, "Transport") | ///
> strpos(major_issues_msa_text, "traffic") | strpos(major_issues_msa_text, "Traffic") 
{txt}
{com}. gen transport_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "government") | strpos(major_issues_msa_text, "Government") | ///
> strpos(major_issues_msa_text, "public") | strpos(major_issues_msa_text, "Public") | ///
> strpos(major_issues_msa_text, "city") | strpos(major_issues_msa_text, "City") | ///
> strpos(major_issues_msa_text, "tax") | strpos(major_issues_msa_text, "Tax") | ///
> strpos(major_issues_msa_text, "area") | strpos(major_issues_msa_text, "Area") | ///
> strpos(major_issues_msa_text, "system") | strpos(major_issues_msa_text, "System") | ///
> strpos(major_issues_msa_text, "local") | strpos(major_issues_msa_text, "Local") | ///
> strpos(major_issues_msa_text, "people") | strpos(major_issues_msa_text, "People") | ///
> strpos(major_issues_msa_text, "issue") | strpos(major_issues_msa_text, "Issue") | ///
> strpos(major_issues_msa_text, "state") | strpos(major_issues_msa_text, "State") 
{txt}
{com}. gen politics_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. gen temp=strpos(major_issues_msa_text, "racial") | strpos(major_issues_msa_text, "Racial") | ///
> strpos(major_issues_msa_text, "racism") | strpos(major_issues_msa_text, "Racism") | ///
> strpos(major_issues_msa_text, "race") | strpos(major_issues_msa_text, "Race") 
{txt}
{com}. gen race_cat=(temp!=0)
{txt}
{com}. drop temp
{txt}
{com}. 
. * Save data with individual indicators for mentioning at least one word from a category
. save word_categories, replace
{txt}file word_categories.dta saved

{com}. 
. 
. 
. 
. ***** Word stats in pooled sample *****
. 
. * Word categories
. use word_categories, clear
{txt}( )

{com}. 
. * Statistics shown in Table A-2 of the appendix
. tabstat crime_cat economy_cat housing_cat education_cat social_cat transport_cat politics_cat race_cat, s(mean)

{txt}   stats {...}
{c |}{...}
  crime_~t  econom~t  housin~t  educat~t  social~t  transp~t  politi~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res} .3034615  .3328205  .1344872  .1024359  .2023077   .100641  .2534615  .0783333
{txt}{hline 9}{c BT}{hline 80}

{com}. 
. * Single words
. use single_words, clear
{txt}( )

{com}. 
. * Statistics shown in Table A-3 of the appendix
. tabstat crime job housing people education lack poverty homeless government transport violence city public unemployment traffic issue living tax drug cost, s(mean)

{txt}   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res} .2098718  .1533333  .0947436  .0719231  .0860256  .0798718  .0655128  .0766667  .0424359
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res} .0473077  .0446154  .0376923  .0423077  .0415385  .0410256  .0442308  .0352564  .0469231
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res} .0535897  .0394872
{txt}{hline 9}{c BT}{hline 20}

{com}. 
. 
. 
. 
. ***** Word  stats by individual MSA *****
. 
. * Word categories
. use word_categories, clear
{txt}( )

{com}. 
. * Statistics shown in Table A-4 of the appendix
. bysort msa: tabstat economy_cat crime_cat politics_cat social_cat housing_cat education_cat transport_cat race_cat, s(mean)

{txt}{hline}
-> msa = Charlott

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .297      .241      .255      .119      .166      .097      .148      .096
{txt}{hline 9}{c BT}{hline 80}

{hline}
-> msa = Clevelan

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .438       .32      .238      .199      .104      .128      .041      .065
{txt}{hline 9}{c BT}{hline 80}

{hline}
-> msa = Houston

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .265       .15      .224      .159      .077      .046      .149      .025
{txt}{hline 9}{c BT}{hline 80}

{hline}
-> msa = Indianap

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .352      .393      .256      .173      .091       .11      .111      .052
{txt}{hline 9}{c BT}{hline 80}

{hline}
-> msa = Memphis

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .318      .483      .206      .196      .049      .151      .024      .134
{txt}{hline 9}{c BT}{hline 80}

{hline}
-> msa = Rocheste

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}    .4775      .255      .295    .25625    .10875    .15625     .0275     .0425
{txt}{hline 9}{c BT}{hline 80}

{hline}
-> msa = St. Loui

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .346      .441      .266      .174      .053      .098       .05      .189
{txt}{hline 9}{c BT}{hline 80}

{hline}
-> msa = Seattle

   stats {...}
{c |}{...}
  econom~t  crime_~t  politi~t  social~t  housin~t  educat~t  transp~t  race_cat
{hline 9}{c +}{hline 80}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .198      .135      .296      .353      .422      .044       .24      .016
{txt}{hline 9}{c BT}{hline 80}

{com}. 
. * Single words
. use single_words, clear
{txt}( )

{com}. 
. bysort msa: tabstat crime job housing people education lack poverty homeless government transport violence city public unemployment traffic issue living tax drug cost, s(mean)

{txt}{hline}
-> msa = Charlott

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}      .16      .128      .125      .074      .063      .066      .024      .034      .028
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .055      .039      .031      .039      .023      .071      .061      .029      .042
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}      .02      .036
{txt}{hline 9}{c BT}{hline 20}

{hline}
-> msa = Clevelan

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .201      .223      .069      .067      .108      .087      .085      .042      .042
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .014      .053      .028      .035      .055      .001      .041      .029      .034
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}      .07      .015
{txt}{hline 9}{c BT}{hline 20}

{hline}
-> msa = Houston

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .113      .109      .049      .074      .043      .045      .032      .087      .037
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .058      .013      .032       .04      .043      .083      .042      .018      .036
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}      .02      .024
{txt}{hline 9}{c BT}{hline 20}

{hline}
-> msa = Indianap

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .275      .165      .056      .077      .096      .071      .056      .048      .034
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .083      .066      .024      .083      .041      .006      .044      .027      .031
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .082      .028
{txt}{hline 9}{c BT}{hline 20}

{hline}
-> msa = Memphis

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .403      .142      .029      .064      .142      .104      .126      .024      .049
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .016      .055      .036      .024      .062      .002      .042      .019      .007
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .033      .006
{txt}{hline 9}{c BT}{hline 20}

{hline}
-> msa = Rocheste

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}    .1575    .26625       .07    .05875    .12375    .11625    .14875       .03    .03625
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}   .02125     .0375    .05375       .03    .05875    .00125      .035    .03375    .10375
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}    .0975    .03125
{txt}{hline 9}{c BT}{hline 20}

{hline}
-> msa = St. Loui

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}       .3      .161      .034      .079       .08      .118      .057      .037      .058
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .022      .085      .058      .034      .046      .008      .049      .014      .021
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .041      .013
{txt}{hline 9}{c BT}{hline 20}

{hline}
-> msa = Seattle

   stats {...}
{c |}{...}
     crime       job   housing    people  educat~n      lack   poverty  homeless  govern~t
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .059      .055      .321      .079       .04      .039      .012      .302      .054
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
  transp~t  violence      city    public  unempl~t   traffic     issue    living       tax
{hline 9}{c +}{hline 90}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .104      .007      .042      .051      .007      .148      .038      .112      .112
{txt}{hline 9}{c BT}{hline 90}

   stats {...}
{c |}{...}
      drug      cost
{hline 9}{c +}{hline 20}
{ralign 8:mean} {...}
{c |}{...}
 {res}     .074      .161
{txt}{hline 9}{c BT}{hline 20}

{com}. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. 
. 
. ******* Code to create Figure 1 *******
. * Runs regressions underlying fig. 1, showing polarization over nat'l issues in our sample
. do "$localdir/Code/polarization_natissues.do"
{txt}
{com}. // Estimate regressions in Figure 1
. // Document polarization in our sample on national policy issues
. 
. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. cd "$localdir/Data"
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Data
{txt}
{com}. gl output "$localdir/Output"
{txt}
{com}. 
. 
. set scheme s1mono
{txt}
{com}. 
. clear all 
{res}{txt}
{com}. set more off
{txt}
{com}. 
. use msa_survey_indiv.dta, clear
{txt}( )

{com}. 
. 
. 
. 
. rename party_id party_id_nolean
{res}{txt}
{com}. clonevar party_id_lean = party_id_nolean
{txt}(469 missing values generated)

{com}. replace party_id_lean = 1 if  q41_b == 2
{txt}(838 real changes made)

{com}. replace party_id_lean = 2 if  q41_b == 1
{txt}(706 real changes made)

{com}. 
. rename party_strong pid7
{res}{txt}
{com}. lab def pid7 1 "Strong Democrat" 2 "Democrat" 3 "Lean Democrat" 4 "Independent" 5 "Lean Republican" 6 "Republican" 7 "Strong Republican"
{txt}
{com}. lab val pid7 pid7
{txt}
{com}. 
. gen dem_lean = party_id_lean == 1
{txt}
{com}. lab var dem_lean "Democrat"
{txt}
{com}. gen rep_lean = party_id_lean == 2
{txt}
{com}. lab var rep_lean "Republican"
{txt}
{com}. gen ind_lean = party_id_lean == 3
{txt}
{com}. lab var ind_lean "Independent"
{txt}
{com}. 
. gen looking_for_work = 0
{txt}
{com}. replace looking_for_work = 1 if empl_status == 4 | empl_status == 3
{txt}(801 real changes made)

{com}. 
. egen years_in_msa2 = cut(years_in_msa), at(0, 1, 5, 10, 15, 100) 
{txt}
{com}. 
. gen homeowner = own_where_living == 1
{txt}
{com}. 
. // generate msa-specific income quartile variable
. levelsof msa
{res}{txt}1 2 3 4 5 6 7 8

{com}. gen inc_quartile = .
{txt}(7,800 missing values generated)

{com}. foreach l in `r(levels)' {c -(}
{txt}  2{com}.         cap drop temp
{txt}  3{com}.         sum income if msa == `l', detail
{txt}  4{com}.         egen temp = cut(income) if msa == `l', at(`r(min)', `r(p25)', `r(p50)', `r(p75)', `r(max)') icodes
{txt}  5{com}.         replace inc_quartile = temp if msa == `l'
{txt}  6{com}. {c )-}

                           {txt}Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        988
{txt}25%    {res}        4              1       {txt}Sum of Wgt. {res}        988

{txt}50%    {res}        6                      {txt}Mean          {res} 5.885628
                        {txt}Largest       Std. Dev.     {res} 3.014332
{txt}75%    {res}        8             16
{txt}90%    {res}       10             16       {txt}Variance      {res} 9.086196
{txt}95%    {res}       11             16       {txt}Skewness      {res} .5474496
{txt}99%    {res}       14             16       {txt}Kurtosis      {res}  3.03241
{txt}(6816 missing values generated)
(984 real changes made)

                           Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        988
{txt}25%    {res}        3              1       {txt}Sum of Wgt. {res}        988

{txt}50%    {res}        5                      {txt}Mean          {res} 5.724696
                        {txt}Largest       Std. Dev.     {res} 3.210784
{txt}75%    {res}        8             16
{txt}90%    {res}       10             16       {txt}Variance      {res} 10.30914
{txt}95%    {res}       12             16       {txt}Skewness      {res} .6430646
{txt}99%    {res}       14             16       {txt}Kurtosis      {res}  3.04975
{txt}(6818 missing values generated)
(982 real changes made)

                           Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        990
{txt}25%    {res}        4              1       {txt}Sum of Wgt. {res}        990

{txt}50%    {res}        6                      {txt}Mean          {res} 6.093939
                        {txt}Largest       Std. Dev.     {res}  3.37993
{txt}75%    {res}        8             16
{txt}90%    {res}       11             16       {txt}Variance      {res} 11.42393
{txt}95%    {res}       12             16       {txt}Skewness      {res} .5479484
{txt}99%    {res}       15             16       {txt}Kurtosis      {res} 2.714609
{txt}(6815 missing values generated)
(985 real changes made)

                           Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        994
{txt}25%    {res}        3              1       {txt}Sum of Wgt. {res}        994

{txt}50%    {res}        6                      {txt}Mean          {res} 5.828974
                        {txt}Largest       Std. Dev.     {res} 2.991247
{txt}75%    {res}        8             14
{txt}90%    {res}       10             15       {txt}Variance      {res} 8.947558
{txt}95%    {res}       11             16       {txt}Skewness      {res} .4420945
{txt}99%    {res}       13             16       {txt}Kurtosis      {res} 2.795217
{txt}(6808 missing values generated)
(992 real changes made)

                           Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        990
{txt}25%    {res}        3              1       {txt}Sum of Wgt. {res}        990

{txt}50%    {res}        5                      {txt}Mean          {res} 5.874747
                        {txt}Largest       Std. Dev.     {res} 3.374871
{txt}75%    {res}        8             16
{txt}90%    {res}       10             16       {txt}Variance      {res} 11.38976
{txt}95%    {res}       12             16       {txt}Skewness      {res} .6464906
{txt}99%    {res}       16             16       {txt}Kurtosis      {res} 3.038548
{txt}(6822 missing values generated)
(978 real changes made)

                           Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        788
{txt}25%    {res}        3              1       {txt}Sum of Wgt. {res}        788

{txt}50%    {res}        5                      {txt}Mean          {res} 5.705584
                        {txt}Largest       Std. Dev.     {res} 2.984436
{txt}75%    {res}        8             14
{txt}90%    {res}       10             14       {txt}Variance      {res} 8.906856
{txt}95%    {res}       11             15       {txt}Skewness      {res} .4495885
{txt}99%    {res}       14             15       {txt}Kurtosis      {res} 2.733502
{txt}(7014 missing values generated)
(786 real changes made)

                           Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        2              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        988
{txt}25%    {res}        4              1       {txt}Sum of Wgt. {res}        988

{txt}50%    {res}        5                      {txt}Mean          {res} 5.851215
                        {txt}Largest       Std. Dev.     {res} 3.097067
{txt}75%    {res}        8             16
{txt}90%    {res}       10             16       {txt}Variance      {res} 9.591822
{txt}95%    {res}       11             16       {txt}Skewness      {res} .6660857
{txt}99%    {res}       15             16       {txt}Kurtosis      {res} 3.288143
{txt}(6820 missing values generated)
(980 real changes made)

                           Salary
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        979
{txt}25%    {res}        4              1       {txt}Sum of Wgt. {res}        979

{txt}50%    {res}        6                      {txt}Mean          {res} 6.431052
                        {txt}Largest       Std. Dev.     {res} 3.462049
{txt}75%    {res}        9             16
{txt}90%    {res}       11             16       {txt}Variance      {res} 11.98578
{txt}95%    {res}       13             16       {txt}Skewness      {res} .3798429
{txt}99%    {res}       15             16       {txt}Kurtosis      {res} 2.569492
{txt}(6828 missing values generated)
(972 real changes made)

{com}. cap drop temp
{txt}
{com}. 
. 
. // Perceptions of the economy - not actually reported in the paper
. foreach stub in us_econ msa_econ personal_fin {c -(}
{txt}  2{com}.         
.         local v = "dissatisfied_" + "`stub'"
{txt}  3{com}.         
.         reg `v'  ind_lean rep_lean [pw=weight], robust
{txt}  4{com}.         estimates store `stub'_biv
{txt}  5{com}.         
.         reg `v' ind_lean rep_lean age3150-agegt65 i.income i.race female college i.msa [pw=weight], robust
{txt}  6{com}.         estimates store `stub'_mult
{txt}  7{com}. {c )-}
{txt}(sum of wgt is 7,800)

Linear regression                               Number of obs     = {res}     7,800
                                                {txt}F(2, 7797)        =  {res}   150.69
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0599
                                                {txt}Root MSE          =    {res} 2.4115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}di~s_economy{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}ind_lean {c |}{col 14}{res}{space 2}-.1323075{col 26}{space 2} .1091486{col 37}{space 1}   -1.21{col 46}{space 3}0.225{col 54}{space 4} -.346268{col 67}{space 3}  .081653
{txt}{space 4}rep_lean {c |}{col 14}{res}{space 2}-1.302688{col 26}{space 2} .0774726{col 37}{space 1}  -16.81{col 46}{space 3}0.000{col 54}{space 4}-1.454555{col 67}{space 3}-1.150821
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.937615{col 26}{space 2} .0516682{col 37}{space 1}  114.92{col 46}{space 3}0.000{col 54}{space 4} 5.836331{col 67}{space 3} 6.038898
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,705.6025721699)

Linear regression                               Number of obs     = {res}     7,696
                                                {txt}F(35, 7660)       =  {res}    17.60
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1064
                                                {txt}Root MSE          =    {res}  2.355

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}   dissatisfied_us_economy{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}ind_lean {c |}{col 28}{res}{space 2}-.2294725{col 40}{space 2}   .11179{col 51}{space 1}   -2.05{col 60}{space 3}0.040{col 68}{space 4}-.4486115{col 81}{space 3}-.0103336
{txt}{space 18}rep_lean {c |}{col 28}{res}{space 2}-1.080586{col 40}{space 2} .0778156{col 51}{space 1}  -13.89{col 60}{space 3}0.000{col 68}{space 4}-1.233125{col 81}{space 3}-.9280456
{txt}{space 19}age3150 {c |}{col 28}{res}{space 2} .0278019{col 40}{space 2} .1020593{col 51}{space 1}    0.27{col 60}{space 3}0.785{col 68}{space 4}-.1722623{col 81}{space 3} .2278661
{txt}{space 19}age5165 {c |}{col 28}{res}{space 2}-.1250777{col 40}{space 2} .1051482{col 51}{space 1}   -1.19{col 60}{space 3}0.234{col 68}{space 4}-.3311969{col 81}{space 3} .0810415
{txt}{space 19}agegt65 {c |}{col 28}{res}{space 2}-.5208457{col 40}{space 2} .1205127{col 51}{space 1}   -4.32{col 60}{space 3}0.000{col 68}{space 4}-.7570835{col 81}{space 3}-.2846078
{txt}{space 26} {c |}
{space 20}income {c |}
{space 8}$10,000 � $19,999  {c |}{col 28}{res}{space 2}-.1059575{col 40}{space 2} .1975631{col 51}{space 1}   -0.54{col 60}{space 3}0.592{col 68}{space 4}-.4932353{col 81}{space 3} .2813204
{txt}{space 8}$20,000 - $29,999  {c |}{col 28}{res}{space 2}-.1415864{col 40}{space 2} .1907259{col 51}{space 1}   -0.74{col 60}{space 3}0.458{col 68}{space 4}-.5154614{col 81}{space 3} .2322886
{txt}{space 8}$30,000 - $39,999  {c |}{col 28}{res}{space 2}-.2656571{col 40}{space 2} .1953598{col 51}{space 1}   -1.36{col 60}{space 3}0.174{col 68}{space 4}-.6486158{col 81}{space 3} .1173016
{txt}{space 8}$40,000 - $54,999  {c |}{col 28}{res}{space 2}-.2494687{col 40}{space 2} .1814424{col 51}{space 1}   -1.37{col 60}{space 3}0.169{col 68}{space 4}-.6051455{col 81}{space 3} .1062082
{txt}{space 8}$55,000 - $69,999  {c |}{col 28}{res}{space 2}-.3843272{col 40}{space 2} .1823488{col 51}{space 1}   -2.11{col 60}{space 3}0.035{col 68}{space 4}-.7417808{col 81}{space 3}-.0268736
{txt}{space 8}$70,000 - $84,999  {c |}{col 28}{res}{space 2}-.7693108{col 40}{space 2}  .185236{col 51}{space 1}   -4.15{col 60}{space 3}0.000{col 68}{space 4}-1.132424{col 81}{space 3}-.4061976
{txt}{space 8}$85,000 � $99,999  {c |}{col 28}{res}{space 2}-.7089377{col 40}{space 2} .1988772{col 51}{space 1}   -3.56{col 60}{space 3}0.000{col 68}{space 4}-1.098791{col 81}{space 3} -.319084
{txt}{space 6}$100,000 - $124,999  {c |}{col 28}{res}{space 2}-.8762141{col 40}{space 2} .1879437{col 51}{space 1}   -4.66{col 60}{space 3}0.000{col 68}{space 4}-1.244635{col 81}{space 3} -.507793
{txt}{space 6}$125,000 - $149,999  {c |}{col 28}{res}{space 2}-.8875999{col 40}{space 2} .2225602{col 51}{space 1}   -3.99{col 60}{space 3}0.000{col 68}{space 4}-1.323879{col 81}{space 3} -.451321
{txt}{space 6}$150,000 - $174,999  {c |}{col 28}{res}{space 2}-.9061326{col 40}{space 2} .2635111{col 51}{space 1}   -3.44{col 60}{space 3}0.001{col 68}{space 4}-1.422687{col 81}{space 3}-.3895787
{txt}{space 6}$175,000 - $199,999  {c |}{col 28}{res}{space 2}-1.090271{col 40}{space 2}  .269042{col 51}{space 1}   -4.05{col 60}{space 3}0.000{col 68}{space 4}-1.617667{col 81}{space 3}-.5628747
{txt}{space 6}$200,000 - $249,999  {c |}{col 28}{res}{space 2}-.4634957{col 40}{space 2} .2862112{col 51}{space 1}   -1.62{col 60}{space 3}0.105{col 68}{space 4}-1.024548{col 81}{space 3} .0975566
{txt}{space 6}$250,000 - $349,999  {c |}{col 28}{res}{space 2}-.8204189{col 40}{space 2} .4566928{col 51}{space 1}   -1.80{col 60}{space 3}0.072{col 68}{space 4}-1.715662{col 81}{space 3}  .074824
{txt}{space 6}$350,000 - $470,000  {c |}{col 28}{res}{space 2}-1.002706{col 40}{space 2} .5180833{col 51}{space 1}   -1.94{col 60}{space 3}0.053{col 68}{space 4}-2.018291{col 81}{space 3} .0128787
{txt}{space 12}Over $470,000  {c |}{col 28}{res}{space 2}-2.159391{col 40}{space 2} .4628135{col 51}{space 1}   -4.67{col 60}{space 3}0.000{col 68}{space 4}-3.066632{col 81}{space 3} -1.25215
{txt}{space 26} {c |}
{space 22}race {c |}
Black or African American  {c |}{col 28}{res}{space 2} .2232019{col 40}{space 2} .1119383{col 51}{space 1}    1.99{col 60}{space 3}0.046{col 68}{space 4} .0037723{col 81}{space 3} .4426315
{txt}{space 7}Hispanic or Latino  {c |}{col 28}{res}{space 2} .0296869{col 40}{space 2} .1885965{col 51}{space 1}    0.16{col 60}{space 3}0.875{col 68}{space 4}-.3400139{col 81}{space 3} .3993876
{txt}{space 2}Asian or Asian-American  {c |}{col 28}{res}{space 2} -.298671{col 40}{space 2} .1604707{col 51}{space 1}   -1.86{col 60}{space 3}0.063{col 68}{space 4}-.6132375{col 81}{space 3} .0158956
{txt}{space 10}Native American  {c |}{col 28}{res}{space 2}-.5436063{col 40}{space 2} .5338889{col 51}{space 1}   -1.02{col 60}{space 3}0.309{col 68}{space 4}-1.590175{col 81}{space 3}  .502962
{txt}{space 15}Mixed Race  {c |}{col 28}{res}{space 2} .4363883{col 40}{space 2} .2185595{col 51}{space 1}    2.00{col 60}{space 3}0.046{col 68}{space 4}  .007952{col 81}{space 3} .8648247
{txt}{space 20}Other  {c |}{col 28}{res}{space 2} .6162496{col 40}{space 2} .4107802{col 51}{space 1}    1.50{col 60}{space 3}0.134{col 68}{space 4}-.1889922{col 81}{space 3} 1.421491
{txt}{space 26} {c |}
{space 20}female {c |}{col 28}{res}{space 2} .4673933{col 40}{space 2} .0724797{col 51}{space 1}    6.45{col 60}{space 3}0.000{col 68}{space 4} .3253134{col 81}{space 3} .6094733
{txt}{space 19}college {c |}{col 28}{res}{space 2}-.2726855{col 40}{space 2} .0701719{col 51}{space 1}   -3.89{col 60}{space 3}0.000{col 68}{space 4}-.4102417{col 81}{space 3}-.1351292
{txt}{space 26} {c |}
{space 23}msa {c |}
{space 16}Cleveland  {c |}{col 28}{res}{space 2} .3082113{col 40}{space 2} .1356695{col 51}{space 1}    2.27{col 60}{space 3}0.023{col 68}{space 4} .0422619{col 81}{space 3} .5741608
{txt}{space 18}Houston  {c |}{col 28}{res}{space 2} .2067219{col 40}{space 2} .1459061{col 51}{space 1}    1.42{col 60}{space 3}0.157{col 68}{space 4} -.079294{col 81}{space 3} .4927379
{txt}{space 13}Indianapolis  {c |}{col 28}{res}{space 2} .2243787{col 40}{space 2}  .140115{col 51}{space 1}    1.60{col 60}{space 3}0.109{col 68}{space 4} -.050285{col 81}{space 3} .4990424
{txt}{space 18}Memphis  {c |}{col 28}{res}{space 2} .3485072{col 40}{space 2} .1533721{col 51}{space 1}    2.27{col 60}{space 3}0.023{col 68}{space 4} .0478558{col 81}{space 3} .6491586
{txt}{space 16}Rochester  {c |}{col 28}{res}{space 2} .3347191{col 40}{space 2} .1414637{col 51}{space 1}    2.37{col 60}{space 3}0.018{col 68}{space 4} .0574116{col 81}{space 3} .6120267
{txt}{space 16}St. Louis  {c |}{col 28}{res}{space 2} .2451092{col 40}{space 2} .1308879{col 51}{space 1}    1.87{col 60}{space 3}0.061{col 68}{space 4}-.0114669{col 81}{space 3} .5016853
{txt}{space 18}Seattle  {c |}{col 28}{res}{space 2} .1251523{col 40}{space 2} .1325197{col 51}{space 1}    0.94{col 60}{space 3}0.345{col 68}{space 4}-.1346226{col 81}{space 3} .3849272
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 5.999903{col 40}{space 2} .2003478{col 51}{space 1}   29.95{col 60}{space 3}0.000{col 68}{space 4} 5.607167{col 81}{space 3}  6.39264
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,800)

Linear regression                               Number of obs     = {res}     7,800
                                                {txt}F(2, 7797)        =  {res}    35.18
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0150
                                                {txt}Root MSE          =    {res} 2.3211

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}di~a_economy{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}ind_lean {c |}{col 14}{res}{space 2} .2140529{col 26}{space 2} .1064853{col 37}{space 1}    2.01{col 46}{space 3}0.044{col 54}{space 4}  .005313{col 67}{space 3} .4227927
{txt}{space 4}rep_lean {c |}{col 14}{res}{space 2}-.5275318{col 26}{space 2} .0751377{col 37}{space 1}   -7.02{col 46}{space 3}0.000{col 54}{space 4}-.6748218{col 67}{space 3}-.3802418
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.681365{col 26}{space 2} .0514295{col 37}{space 1}  110.47{col 46}{space 3}0.000{col 54}{space 4} 5.580549{col 67}{space 3}  5.78218
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,705.6025721699)

Linear regression                               Number of obs     = {res}     7,696
                                                {txt}F(35, 7660)       =  {res}    12.62
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0852
                                                {txt}Root MSE          =    {res} 2.2408

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}  dissatisfied_msa_economy{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}ind_lean {c |}{col 28}{res}{space 2} .1711185{col 40}{space 2} .1095645{col 51}{space 1}    1.56{col 60}{space 3}0.118{col 68}{space 4}-.0436579{col 81}{space 3} .3858949
{txt}{space 18}rep_lean {c |}{col 28}{res}{space 2}-.3651072{col 40}{space 2} .0770128{col 51}{space 1}   -4.74{col 60}{space 3}0.000{col 68}{space 4}-.5160734{col 81}{space 3} -.214141
{txt}{space 19}age3150 {c |}{col 28}{res}{space 2} .1153263{col 40}{space 2} .1000641{col 51}{space 1}    1.15{col 60}{space 3}0.249{col 68}{space 4}-.0808266{col 81}{space 3} .3114793
{txt}{space 19}age5165 {c |}{col 28}{res}{space 2} .1673322{col 40}{space 2} .1000519{col 51}{space 1}    1.67{col 60}{space 3}0.094{col 68}{space 4} -.028797{col 81}{space 3} .3634613
{txt}{space 19}agegt65 {c |}{col 28}{res}{space 2}-.2628688{col 40}{space 2} .1141927{col 51}{space 1}   -2.30{col 60}{space 3}0.021{col 68}{space 4}-.4867177{col 81}{space 3}-.0390198
{txt}{space 26} {c |}
{space 20}income {c |}
{space 8}$10,000 � $19,999  {c |}{col 28}{res}{space 2} .2265623{col 40}{space 2} .1881926{col 51}{space 1}    1.20{col 60}{space 3}0.229{col 68}{space 4}-.1423468{col 81}{space 3} .5954714
{txt}{space 8}$20,000 - $29,999  {c |}{col 28}{res}{space 2}-.0080818{col 40}{space 2} .1769095{col 51}{space 1}   -0.05{col 60}{space 3}0.964{col 68}{space 4}-.3548728{col 81}{space 3} .3387092
{txt}{space 8}$30,000 - $39,999  {c |}{col 28}{res}{space 2}-.1010731{col 40}{space 2} .1878975{col 51}{space 1}   -0.54{col 60}{space 3}0.591{col 68}{space 4}-.4694036{col 81}{space 3} .2672575
{txt}{space 8}$40,000 - $54,999  {c |}{col 28}{res}{space 2}-.0524329{col 40}{space 2} .1755449{col 51}{space 1}   -0.30{col 60}{space 3}0.765{col 68}{space 4} -.396549{col 81}{space 3} .2916832
{txt}{space 8}$55,000 - $69,999  {c |}{col 28}{res}{space 2}-.0864978{col 40}{space 2}  .175258{col 51}{space 1}   -0.49{col 60}{space 3}0.622{col 68}{space 4}-.4300515{col 81}{space 3} .2570558
{txt}{space 8}$70,000 - $84,999  {c |}{col 28}{res}{space 2}-.2718544{col 40}{space 2} .1776341{col 51}{space 1}   -1.53{col 60}{space 3}0.126{col 68}{space 4}-.6200659{col 81}{space 3} .0763571
{txt}{space 8}$85,000 � $99,999  {c |}{col 28}{res}{space 2}-.3478395{col 40}{space 2} .1967439{col 51}{space 1}   -1.77{col 60}{space 3}0.077{col 68}{space 4}-.7335114{col 81}{space 3} .0378324
{txt}{space 6}$100,000 - $124,999  {c |}{col 28}{res}{space 2}-.5625778{col 40}{space 2}   .18461{col 51}{space 1}   -3.05{col 60}{space 3}0.002{col 68}{space 4} -.924464{col 81}{space 3}-.2006916
{txt}{space 6}$125,000 - $149,999  {c |}{col 28}{res}{space 2}-.6285292{col 40}{space 2} .2272118{col 51}{space 1}   -2.77{col 60}{space 3}0.006{col 68}{space 4}-1.073927{col 81}{space 3}-.1831318
{txt}{space 6}$150,000 - $174,999  {c |}{col 28}{res}{space 2}-.5999523{col 40}{space 2} .2368565{col 51}{space 1}   -2.53{col 60}{space 3}0.011{col 68}{space 4}-1.064256{col 81}{space 3}-.1356488
{txt}{space 6}$175,000 - $199,999  {c |}{col 28}{res}{space 2}-.6201526{col 40}{space 2} .2518711{col 51}{space 1}   -2.46{col 60}{space 3}0.014{col 68}{space 4}-1.113889{col 81}{space 3}-.1264162
{txt}{space 6}$200,000 - $249,999  {c |}{col 28}{res}{space 2}-.2905057{col 40}{space 2} .2725866{col 51}{space 1}   -1.07{col 60}{space 3}0.287{col 68}{space 4}  -.82485{col 81}{space 3} .2438386
{txt}{space 6}$250,000 - $349,999  {c |}{col 28}{res}{space 2}-.4430352{col 40}{space 2} .4432219{col 51}{space 1}   -1.00{col 60}{space 3}0.318{col 68}{space 4}-1.311871{col 81}{space 3} .4258009
{txt}{space 6}$350,000 - $470,000  {c |}{col 28}{res}{space 2}-1.283722{col 40}{space 2} .4982622{col 51}{space 1}   -2.58{col 60}{space 3}0.010{col 68}{space 4}-2.260452{col 81}{space 3}-.3069916
{txt}{space 12}Over $470,000  {c |}{col 28}{res}{space 2}-1.343442{col 40}{space 2} .5180595{col 51}{space 1}   -2.59{col 60}{space 3}0.010{col 68}{space 4} -2.35898{col 81}{space 3}-.3279035
{txt}{space 26} {c |}
{space 22}race {c |}
Black or African American  {c |}{col 28}{res}{space 2} .1686751{col 40}{space 2} .1057526{col 51}{space 1}    1.59{col 60}{space 3}0.111{col 68}{space 4}-.0386289{col 81}{space 3} .3759791
{txt}{space 7}Hispanic or Latino  {c |}{col 28}{res}{space 2} .0578937{col 40}{space 2} .1935233{col 51}{space 1}    0.30{col 60}{space 3}0.765{col 68}{space 4} -.321465{col 81}{space 3} .4372524
{txt}{space 2}Asian or Asian-American  {c |}{col 28}{res}{space 2}-.2727106{col 40}{space 2} .1497731{col 51}{space 1}   -1.82{col 60}{space 3}0.069{col 68}{space 4}-.5663069{col 81}{space 3} .0208856
{txt}{space 10}Native American  {c |}{col 28}{res}{space 2}-.2190262{col 40}{space 2} .4194557{col 51}{space 1}   -0.52{col 60}{space 3}0.602{col 68}{space 4}-1.041274{col 81}{space 3} .6032217
{txt}{space 15}Mixed Race  {c |}{col 28}{res}{space 2} .4073266{col 40}{space 2} .2390393{col 51}{space 1}    1.70{col 60}{space 3}0.088{col 68}{space 4}-.0612559{col 81}{space 3}  .875909
{txt}{space 20}Other  {c |}{col 28}{res}{space 2} .9143753{col 40}{space 2} .3784581{col 51}{space 1}    2.42{col 60}{space 3}0.016{col 68}{space 4} .1724938{col 81}{space 3} 1.656257
{txt}{space 26} {c |}
{space 20}female {c |}{col 28}{res}{space 2} .3319735{col 40}{space 2} .0705755{col 51}{space 1}    4.70{col 60}{space 3}0.000{col 68}{space 4} .1936262{col 81}{space 3} .4703208
{txt}{space 19}college {c |}{col 28}{res}{space 2}-.1963195{col 40}{space 2}  .067261{col 51}{space 1}   -2.92{col 60}{space 3}0.004{col 68}{space 4}-.3281695{col 81}{space 3}-.0644695
{txt}{space 26} {c |}
{space 23}msa {c |}
{space 16}Cleveland  {c |}{col 28}{res}{space 2}   .96121{col 40}{space 2} .1286639{col 51}{space 1}    7.47{col 60}{space 3}0.000{col 68}{space 4} .7089935{col 81}{space 3} 1.213426
{txt}{space 18}Houston  {c |}{col 28}{res}{space 2} .1630217{col 40}{space 2} .1481799{col 51}{space 1}    1.10{col 60}{space 3}0.271{col 68}{space 4}-.1274514{col 81}{space 3} .4534948
{txt}{space 13}Indianapolis  {c |}{col 28}{res}{space 2} .4876743{col 40}{space 2}  .130363{col 51}{space 1}    3.74{col 60}{space 3}0.000{col 68}{space 4} .2321271{col 81}{space 3} .7432215
{txt}{space 18}Memphis  {c |}{col 28}{res}{space 2} 1.341602{col 40}{space 2} .1507587{col 51}{space 1}    8.90{col 60}{space 3}0.000{col 68}{space 4} 1.046074{col 81}{space 3}  1.63713
{txt}{space 16}Rochester  {c |}{col 28}{res}{space 2} 1.165343{col 40}{space 2} .1306528{col 51}{space 1}    8.92{col 60}{space 3}0.000{col 68}{space 4} .9092282{col 81}{space 3} 1.421459
{txt}{space 16}St. Louis  {c |}{col 28}{res}{space 2} .9964685{col 40}{space 2} .1198816{col 51}{space 1}    8.31{col 60}{space 3}0.000{col 68}{space 4} .7614677{col 81}{space 3} 1.231469
{txt}{space 18}Seattle  {c |}{col 28}{res}{space 2} .4633655{col 40}{space 2}  .132384{col 51}{space 1}    3.50{col 60}{space 3}0.000{col 68}{space 4} .2038567{col 81}{space 3} .7228743
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 4.922842{col 40}{space 2} .1910984{col 51}{space 1}   25.76{col 60}{space 3}0.000{col 68}{space 4} 4.548237{col 81}{space 3} 5.297447
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,800)

Linear regression                               Number of obs     = {res}     7,800
                                                {txt}F(2, 7797)        =  {res}    56.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0231
                                                {txt}Root MSE          =    {res} 2.5797

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}dissatisfi~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}ind_lean {c |}{col 14}{res}{space 2} .2635861{col 26}{space 2} .1119266{col 37}{space 1}    2.35{col 46}{space 3}0.019{col 54}{space 4} .0441799{col 67}{space 3} .4829923
{txt}{space 4}rep_lean {c |}{col 14}{res}{space 2} -.744078{col 26}{space 2} .0823736{col 37}{space 1}   -9.03{col 46}{space 3}0.000{col 54}{space 4}-.9055522{col 67}{space 3}-.5826037
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.814228{col 26}{space 2} .0551099{col 37}{space 1}  105.50{col 46}{space 3}0.000{col 54}{space 4} 5.706198{col 67}{space 3} 5.922258
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,705.6025721699)

Linear regression                               Number of obs     = {res}     7,696
                                                {txt}F(35, 7660)       =  {res}    25.24
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1388
                                                {txt}Root MSE          =    {res} 2.4287

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}dissatisfied_personal_fi~s{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}ind_lean {c |}{col 28}{res}{space 2} .0390232{col 40}{space 2} .1143203{col 51}{space 1}    0.34{col 60}{space 3}0.733{col 68}{space 4}-.1850759{col 81}{space 3} .2631223
{txt}{space 18}rep_lean {c |}{col 28}{res}{space 2}-.4699656{col 40}{space 2} .0805357{col 51}{space 1}   -5.84{col 60}{space 3}0.000{col 68}{space 4}-.6278375{col 81}{space 3}-.3120936
{txt}{space 19}age3150 {c |}{col 28}{res}{space 2} .0325737{col 40}{space 2} .1027397{col 51}{space 1}    0.32{col 60}{space 3}0.751{col 68}{space 4}-.1688241{col 81}{space 3} .2339716
{txt}{space 19}age5165 {c |}{col 28}{res}{space 2}-.1797823{col 40}{space 2} .1055818{col 51}{space 1}   -1.70{col 60}{space 3}0.089{col 68}{space 4}-.3867515{col 81}{space 3} .0271868
{txt}{space 19}agegt65 {c |}{col 28}{res}{space 2}-.9495616{col 40}{space 2} .1220504{col 51}{space 1}   -7.78{col 60}{space 3}0.000{col 68}{space 4}-1.188814{col 81}{space 3}-.7103093
{txt}{space 26} {c |}
{space 20}income {c |}
{space 8}$10,000 � $19,999  {c |}{col 28}{res}{space 2}-.1930517{col 40}{space 2} .1991906{col 51}{space 1}   -0.97{col 60}{space 3}0.332{col 68}{space 4}-.5835198{col 81}{space 3} .1974163
{txt}{space 8}$20,000 - $29,999  {c |}{col 28}{res}{space 2}-.2232162{col 40}{space 2} .1924534{col 51}{space 1}   -1.16{col 60}{space 3}0.246{col 68}{space 4}-.6004775{col 81}{space 3} .1540451
{txt}{space 8}$30,000 - $39,999  {c |}{col 28}{res}{space 2}-.8340784{col 40}{space 2} .1979325{col 51}{space 1}   -4.21{col 60}{space 3}0.000{col 68}{space 4} -1.22208{col 81}{space 3}-.4460765
{txt}{space 8}$40,000 - $54,999  {c |}{col 28}{res}{space 2}-.8981371{col 40}{space 2} .1746213{col 51}{space 1}   -5.14{col 60}{space 3}0.000{col 68}{space 4}-1.240443{col 81}{space 3}-.5558315
{txt}{space 8}$55,000 - $69,999  {c |}{col 28}{res}{space 2}-1.038202{col 40}{space 2} .1850151{col 51}{space 1}   -5.61{col 60}{space 3}0.000{col 68}{space 4}-1.400882{col 81}{space 3}-.6755217
{txt}{space 8}$70,000 - $84,999  {c |}{col 28}{res}{space 2}-1.448798{col 40}{space 2}  .187626{col 51}{space 1}   -7.72{col 60}{space 3}0.000{col 68}{space 4}-1.816596{col 81}{space 3}   -1.081
{txt}{space 8}$85,000 � $99,999  {c |}{col 28}{res}{space 2}-1.575383{col 40}{space 2} .2028696{col 51}{space 1}   -7.77{col 60}{space 3}0.000{col 68}{space 4}-1.973063{col 81}{space 3}-1.177703
{txt}{space 6}$100,000 - $124,999  {c |}{col 28}{res}{space 2}-1.971098{col 40}{space 2} .1883932{col 51}{space 1}  -10.46{col 60}{space 3}0.000{col 68}{space 4}  -2.3404{col 81}{space 3}-1.601796
{txt}{space 6}$125,000 - $149,999  {c |}{col 28}{res}{space 2}-2.250147{col 40}{space 2} .2052612{col 51}{space 1}  -10.96{col 60}{space 3}0.000{col 68}{space 4}-2.652516{col 81}{space 3}-1.847779
{txt}{space 6}$150,000 - $174,999  {c |}{col 28}{res}{space 2}-2.029251{col 40}{space 2} .2452273{col 51}{space 1}   -8.27{col 60}{space 3}0.000{col 68}{space 4}-2.509964{col 81}{space 3}-1.548538
{txt}{space 6}$175,000 - $199,999  {c |}{col 28}{res}{space 2}-2.748705{col 40}{space 2} .2658781{col 51}{space 1}  -10.34{col 60}{space 3}0.000{col 68}{space 4}-3.269899{col 81}{space 3}-2.227511
{txt}{space 6}$200,000 - $249,999  {c |}{col 28}{res}{space 2}-2.259658{col 40}{space 2} .2877524{col 51}{space 1}   -7.85{col 60}{space 3}0.000{col 68}{space 4}-2.823732{col 81}{space 3}-1.695585
{txt}{space 6}$250,000 - $349,999  {c |}{col 28}{res}{space 2}-1.814812{col 40}{space 2} .3934955{col 51}{space 1}   -4.61{col 60}{space 3}0.000{col 68}{space 4}-2.586171{col 81}{space 3}-1.043454
{txt}{space 6}$350,000 - $470,000  {c |}{col 28}{res}{space 2}-3.186816{col 40}{space 2} .4722548{col 51}{space 1}   -6.75{col 60}{space 3}0.000{col 68}{space 4}-4.112564{col 81}{space 3}-2.261067
{txt}{space 12}Over $470,000  {c |}{col 28}{res}{space 2}-3.512663{col 40}{space 2} .5267663{col 51}{space 1}   -6.67{col 60}{space 3}0.000{col 68}{space 4}-4.545269{col 81}{space 3}-2.480057
{txt}{space 26} {c |}
{space 22}race {c |}
Black or African American  {c |}{col 28}{res}{space 2} .0028416{col 40}{space 2} .1180692{col 51}{space 1}    0.02{col 60}{space 3}0.981{col 68}{space 4}-.2286064{col 81}{space 3} .2342896
{txt}{space 7}Hispanic or Latino  {c |}{col 28}{res}{space 2}-.0773286{col 40}{space 2} .1880778{col 51}{space 1}   -0.41{col 60}{space 3}0.681{col 68}{space 4}-.4460126{col 81}{space 3} .2913555
{txt}{space 2}Asian or Asian-American  {c |}{col 28}{res}{space 2}-.4088313{col 40}{space 2} .1619827{col 51}{space 1}   -2.52{col 60}{space 3}0.012{col 68}{space 4}-.7263617{col 81}{space 3}-.0913009
{txt}{space 10}Native American  {c |}{col 28}{res}{space 2}-.9488752{col 40}{space 2} .4844869{col 51}{space 1}   -1.96{col 60}{space 3}0.050{col 68}{space 4}-1.898602{col 81}{space 3} .0008519
{txt}{space 15}Mixed Race  {c |}{col 28}{res}{space 2} .3478776{col 40}{space 2} .2246823{col 51}{space 1}    1.55{col 60}{space 3}0.122{col 68}{space 4}-.0925612{col 81}{space 3} .7883164
{txt}{space 20}Other  {c |}{col 28}{res}{space 2} .2785194{col 40}{space 2} .3087436{col 51}{space 1}    0.90{col 60}{space 3}0.367{col 68}{space 4}-.3267026{col 81}{space 3} .8837414
{txt}{space 26} {c |}
{space 20}female {c |}{col 28}{res}{space 2}    .2883{col 40}{space 2} .0742711{col 51}{space 1}    3.88{col 60}{space 3}0.000{col 68}{space 4} .1427084{col 81}{space 3} .4338916
{txt}{space 19}college {c |}{col 28}{res}{space 2} -.325882{col 40}{space 2} .0721737{col 51}{space 1}   -4.52{col 60}{space 3}0.000{col 68}{space 4}-.4673622{col 81}{space 3}-.1844017
{txt}{space 26} {c |}
{space 23}msa {c |}
{space 16}Cleveland  {c |}{col 28}{res}{space 2} .0941966{col 40}{space 2} .1472714{col 51}{space 1}    0.64{col 60}{space 3}0.522{col 68}{space 4}-.1944956{col 81}{space 3} .3828888
{txt}{space 18}Houston  {c |}{col 28}{res}{space 2} .0895683{col 40}{space 2} .1548254{col 51}{space 1}    0.58{col 60}{space 3}0.563{col 68}{space 4}-.2139318{col 81}{space 3} .3930684
{txt}{space 13}Indianapolis  {c |}{col 28}{res}{space 2} .0829388{col 40}{space 2}   .14543{col 51}{space 1}    0.57{col 60}{space 3}0.568{col 68}{space 4}-.2021439{col 81}{space 3} .3680214
{txt}{space 18}Memphis  {c |}{col 28}{res}{space 2} .1087641{col 40}{space 2} .1613196{col 51}{space 1}    0.67{col 60}{space 3}0.500{col 68}{space 4}-.2074663{col 81}{space 3} .4249946
{txt}{space 16}Rochester  {c |}{col 28}{res}{space 2} .0229385{col 40}{space 2} .1561924{col 51}{space 1}    0.15{col 60}{space 3}0.883{col 68}{space 4}-.2832414{col 81}{space 3} .3291184
{txt}{space 16}St. Louis  {c |}{col 28}{res}{space 2} .2421274{col 40}{space 2}  .144653{col 51}{space 1}    1.67{col 60}{space 3}0.094{col 68}{space 4}-.0414321{col 81}{space 3}  .525687
{txt}{space 18}Seattle  {c |}{col 28}{res}{space 2} .0467777{col 40}{space 2}  .139878{col 51}{space 1}    0.33{col 60}{space 3}0.738{col 68}{space 4}-.2274214{col 81}{space 3} .3209768
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 6.885231{col 40}{space 2} .2030989{col 51}{space 1}   33.90{col 60}{space 3}0.000{col 68}{space 4} 6.487102{col 81}{space 3} 7.283361
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. coefplot (us_econ_biv, msymbol(circle) mcolor(black) label(US Econ w/o controls)) ///
>                  (us_econ_mult, msymbol(circle) mcolor(gs10) label(US Econ - w/ controls)) ///
>                  (msa_econ_biv, msymbol(square) mcolor(black) label(MSA Econ - w/o controls)) ///
>                  (msa_econ_mult, msymbol(square) mcolor(gs10) label(MSA Econ - w/o controls)), ///
>                  keep(*lean) xline(0, lpattern(dot))  ///
>                  title("Partisan bias in evaluation of the economy") /// 
>                  xtitle("Dissatisfaction with the economy relative to Democrats (1-10 scale)")
{res}{txt}
{com}. *graph export figs/econ_dissatisfaction_partisan.pdf, replace
. 
. 
. 
. // National policy 
. // make these items binary for ease of interpretation
. gen ineq_smaller_bin = inequality_smaller == 1 | inequality_smaller == 2
{txt}
{com}. lab var ineq_smaller_bin "Want more/same inequality"
{txt}
{com}. 
. gen safety_net_bin = gov_more_safety_net == 1 | gov_more_safety_net == 2
{txt}
{com}. lab var safety_net_bin "Wants to spend less on safety net"
{txt}
{com}. 
. gen reduce_trade_bin = reduce_trade == 4 | reduce_trade == 5
{txt}
{com}. lab var reduce_trade_bin "Wants to reduce trade"
{txt}
{com}. 
. gen for_investment_bin = for_investment == 2
{txt}
{com}. lab var for_investment_bin "Wants to reduce foreign investment" 
{txt}
{com}. 
. gen reduce_immig_bin = reduce_immig == 4 | reduce_immig == 5
{txt}
{com}. lab var reduce_immig_bin "Wants to reduce immigration"
{txt}
{com}. 
. 
. local ind_covs = "i.pid7 age3150 age5165 agegt65 black latino other female college i.inc_quartile looking_for_work homeowner i.years_in_msa2 i.msa"
{txt}
{com}. 
. mata: b = J(5, 3, 0)
{res}{txt}
{com}. local i = 1
{txt}
{com}. foreach v of varlist ineq_smaller_bin safety_net_bin reduce_trade_bin for_investment_bin reduce_immig_bin {c -(}
{txt}  2{com}.         reg `v'  i.pid7 [pw=weight], robust
{txt}  3{com}.         estimates store `v'_biv
{txt}  4{com}.         
.         reg `v' `ind_covs', robust
{txt}  5{com}.         estimates store `v'_mult
{txt}  6{com}.         
.         local b = _b[5.pid7]
{txt}  7{com}.         local se = _se[5.pid7]
{txt}  8{com}.         
.         mata: b[`i', 1] = `i'
{txt}  9{com}.         mata: b[`i', 2] = `b'
{txt} 10{com}.         mata: b[`i', 3] = `se'
{txt} 11{com}.         
.         local ++i
{txt} 12{com}. {c )-}
{txt}(sum of wgt is 7,409.50084159002)

Linear regression                               Number of obs     = {res}     7,331
                                                {txt}F(6, 7324)        =  {res}    43.56
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0523
                                                {txt}Root MSE          =    {res} .47964

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}  ineq_smaller_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2}-.0003902{col 32}{space 2} .0248687{col 43}{space 1}   -0.02{col 52}{space 3}0.987{col 60}{space 4}-.0491401{col 73}{space 3} .0483596
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2}-.0914787{col 32}{space 2} .0261836{col 43}{space 1}   -3.49{col 52}{space 3}0.000{col 60}{space 4}-.1428061{col 73}{space 3}-.0401514
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .1429927{col 32}{space 2} .0249645{col 43}{space 1}    5.73{col 52}{space 3}0.000{col 60}{space 4} .0940551{col 73}{space 3} .1919303
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .1870748{col 32}{space 2} .0291741{col 43}{space 1}    6.41{col 52}{space 3}0.000{col 60}{space 4} .1298852{col 73}{space 3} .2442644
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1124299{col 32}{space 2} .0255128{col 43}{space 1}    4.41{col 52}{space 3}0.000{col 60}{space 4} .0624174{col 73}{space 3} .1624423
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .2727761{col 32}{space 2}  .023941{col 43}{space 1}   11.39{col 52}{space 3}0.000{col 60}{space 4} .2258448{col 73}{space 3} .3197074
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .3272399{col 32}{space 2} .0160453{col 43}{space 1}   20.39{col 52}{space 3}0.000{col 60}{space 4} .2957866{col 73}{space 3} .3586932
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}     7,200
                                                {txt}F(29, 7170)       =  {res}    35.89
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1105
                                                {txt}Root MSE          =    {res}  .4611

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}  ineq_smaller_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0181645{col 32}{space 2} .0171499{col 43}{space 1}    1.06{col 52}{space 3}0.290{col 60}{space 4}-.0154544{col 73}{space 3} .0517833
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2}-.0392859{col 32}{space 2} .0180575{col 43}{space 1}   -2.18{col 52}{space 3}0.030{col 60}{space 4}-.0746839{col 73}{space 3}-.0038879
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .1422212{col 32}{space 2} .0181642{col 43}{space 1}    7.83{col 52}{space 3}0.000{col 60}{space 4}  .106614{col 73}{space 3} .1778284
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2}  .265729{col 32}{space 2} .0236906{col 43}{space 1}   11.22{col 52}{space 3}0.000{col 60}{space 4} .2192884{col 73}{space 3} .3121696
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1984782{col 32}{space 2} .0198906{col 43}{space 1}    9.98{col 52}{space 3}0.000{col 60}{space 4} .1594868{col 73}{space 3} .2374697
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .3791381{col 32}{space 2} .0188037{col 43}{space 1}   20.16{col 52}{space 3}0.000{col 60}{space 4} .3422773{col 73}{space 3} .4159988
{txt}{space 18} {c |}
{space 11}age3150 {c |}{col 20}{res}{space 2} .0068576{col 32}{space 2} .0155506{col 43}{space 1}    0.44{col 52}{space 3}0.659{col 60}{space 4}-.0236262{col 73}{space 3} .0373414
{txt}{space 11}age5165 {c |}{col 20}{res}{space 2} -.035566{col 32}{space 2} .0168696{col 43}{space 1}   -2.11{col 52}{space 3}0.035{col 60}{space 4}-.0686354{col 73}{space 3}-.0024966
{txt}{space 11}agegt65 {c |}{col 20}{res}{space 2}-.0511945{col 32}{space 2} .0193357{col 43}{space 1}   -2.65{col 52}{space 3}0.008{col 60}{space 4}-.0890983{col 73}{space 3}-.0132907
{txt}{space 13}black {c |}{col 20}{res}{space 2} .1979917{col 32}{space 2} .0177143{col 43}{space 1}   11.18{col 52}{space 3}0.000{col 60}{space 4} .1632665{col 73}{space 3}  .232717
{txt}{space 12}latino {c |}{col 20}{res}{space 2} .0771303{col 32}{space 2} .0268496{col 43}{space 1}    2.87{col 52}{space 3}0.004{col 60}{space 4} .0244972{col 73}{space 3} .1297634
{txt}{space 13}other {c |}{col 20}{res}{space 2} .0761634{col 32}{space 2} .0231853{col 43}{space 1}    3.28{col 52}{space 3}0.001{col 60}{space 4} .0307134{col 73}{space 3} .1216135
{txt}{space 12}female {c |}{col 20}{res}{space 2}-.0171274{col 32}{space 2} .0113379{col 43}{space 1}   -1.51{col 52}{space 3}0.131{col 60}{space 4}-.0393529{col 73}{space 3} .0050982
{txt}{space 11}college {c |}{col 20}{res}{space 2}-.0719138{col 32}{space 2} .0119406{col 43}{space 1}   -6.02{col 52}{space 3}0.000{col 60}{space 4}-.0953209{col 73}{space 3}-.0485068
{txt}{space 18} {c |}
{space 6}inc_quartile {c |}
{space 16}1  {c |}{col 20}{res}{space 2}-.0871927{col 32}{space 2} .0179481{col 43}{space 1}   -4.86{col 52}{space 3}0.000{col 60}{space 4}-.1223762{col 73}{space 3}-.0520092
{txt}{space 16}2  {c |}{col 20}{res}{space 2}-.1066754{col 32}{space 2} .0175296{col 43}{space 1}   -6.09{col 52}{space 3}0.000{col 60}{space 4}-.1410386{col 73}{space 3}-.0723122
{txt}{space 16}3  {c |}{col 20}{res}{space 2} -.079804{col 32}{space 2} .0189621{col 43}{space 1}   -4.21{col 52}{space 3}0.000{col 60}{space 4}-.1169754{col 73}{space 3}-.0426326
{txt}{space 18} {c |}
{space 2}looking_for_work {c |}{col 20}{res}{space 2} .0044671{col 32}{space 2} .0189744{col 43}{space 1}    0.24{col 52}{space 3}0.814{col 60}{space 4}-.0327283{col 73}{space 3} .0416625
{txt}{space 9}homeowner {c |}{col 20}{res}{space 2} .0101115{col 32}{space 2} .0134267{col 43}{space 1}    0.75{col 52}{space 3}0.451{col 60}{space 4}-.0162089{col 73}{space 3} .0364318
{txt}{space 18} {c |}
{space 5}years_in_msa2 {c |}
{space 16}5  {c |}{col 20}{res}{space 2}-.0096909{col 32}{space 2} .0266102{col 43}{space 1}   -0.36{col 52}{space 3}0.716{col 60}{space 4}-.0618547{col 73}{space 3} .0424728
{txt}{space 15}10  {c |}{col 20}{res}{space 2}-.0301178{col 32}{space 2}  .027634{col 43}{space 1}   -1.09{col 52}{space 3}0.276{col 60}{space 4}-.0842885{col 73}{space 3}  .024053
{txt}{space 15}15  {c |}{col 20}{res}{space 2} .0041757{col 32}{space 2} .0187069{col 43}{space 1}    0.22{col 52}{space 3}0.823{col 60}{space 4}-.0324953{col 73}{space 3} .0408467
{txt}{space 18} {c |}
{space 15}msa {c |}
{space 8}Cleveland  {c |}{col 20}{res}{space 2} .0443806{col 32}{space 2} .0221981{col 43}{space 1}    2.00{col 52}{space 3}0.046{col 60}{space 4} .0008657{col 73}{space 3} .0878954
{txt}{space 10}Houston  {c |}{col 20}{res}{space 2}  .059322{col 32}{space 2} .0227644{col 43}{space 1}    2.61{col 52}{space 3}0.009{col 60}{space 4} .0146971{col 73}{space 3}  .103947
{txt}{space 5}Indianapolis  {c |}{col 20}{res}{space 2}-.0091962{col 32}{space 2} .0213656{col 43}{space 1}   -0.43{col 52}{space 3}0.667{col 60}{space 4}-.0510791{col 73}{space 3} .0326868
{txt}{space 10}Memphis  {c |}{col 20}{res}{space 2} .0099001{col 32}{space 2} .0225901{col 43}{space 1}    0.44{col 52}{space 3}0.661{col 60}{space 4}-.0343832{col 73}{space 3} .0541834
{txt}{space 8}Rochester  {c |}{col 20}{res}{space 2}-.0089652{col 32}{space 2}  .022941{col 43}{space 1}   -0.39{col 52}{space 3}0.696{col 60}{space 4}-.0539364{col 73}{space 3} .0360059
{txt}{space 8}St. Louis  {c |}{col 20}{res}{space 2} .0241872{col 32}{space 2} .0219523{col 43}{space 1}    1.10{col 52}{space 3}0.271{col 60}{space 4}-.0188458{col 73}{space 3} .0672201
{txt}{space 10}Seattle  {c |}{col 20}{res}{space 2}-.0060202{col 32}{space 2} .0217518{col 43}{space 1}   -0.28{col 52}{space 3}0.782{col 60}{space 4}-.0486601{col 73}{space 3} .0366197
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .3398225{col 32}{space 2} .0301957{col 43}{space 1}   11.25{col 52}{space 3}0.000{col 60}{space 4} .2806301{col 73}{space 3}  .399015
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,409.50084159002)

Linear regression                               Number of obs     = {res}     7,331
                                                {txt}F(6, 7324)        =  {res}    65.97
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0763
                                                {txt}Root MSE          =    {res} .29743

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    safety_net_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0182828{col 32}{space 2} .0073672{col 43}{space 1}    2.48{col 52}{space 3}0.013{col 60}{space 4}  .003841{col 73}{space 3} .0327246
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2} .0344641{col 32}{space 2} .0102408{col 43}{space 1}    3.37{col 52}{space 3}0.001{col 60}{space 4} .0143891{col 73}{space 3} .0545391
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .0837302{col 32}{space 2} .0116555{col 43}{space 1}    7.18{col 52}{space 3}0.000{col 60}{space 4} .0608821{col 73}{space 3} .1065784
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .2356701{col 32}{space 2} .0218405{col 43}{space 1}   10.79{col 52}{space 3}0.000{col 60}{space 4} .1928564{col 73}{space 3} .2784839
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1512119{col 32}{space 2} .0143039{col 43}{space 1}   10.57{col 52}{space 3}0.000{col 60}{space 4} .1231722{col 73}{space 3} .1792516
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .2085114{col 32}{space 2} .0156714{col 43}{space 1}   13.31{col 52}{space 3}0.000{col 60}{space 4} .1777909{col 73}{space 3} .2392318
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}  .017093{col 32}{space 2} .0038244{col 43}{space 1}    4.47{col 52}{space 3}0.000{col 60}{space 4} .0095961{col 73}{space 3} .0245898
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}     7,200
                                                {txt}F(29, 7170)       =  {res}    21.87
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0904
                                                {txt}Root MSE          =    {res} .30134

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    safety_net_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0168746{col 32}{space 2} .0070699{col 43}{space 1}    2.39{col 52}{space 3}0.017{col 60}{space 4} .0030155{col 73}{space 3} .0307338
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2} .0220397{col 32}{space 2} .0089343{col 43}{space 1}    2.47{col 52}{space 3}0.014{col 60}{space 4} .0045259{col 73}{space 3} .0395534
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .0859737{col 32}{space 2} .0100477{col 43}{space 1}    8.56{col 52}{space 3}0.000{col 60}{space 4} .0662772{col 73}{space 3} .1056702
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .2070717{col 32}{space 2} .0180589{col 43}{space 1}   11.47{col 52}{space 3}0.000{col 60}{space 4}  .171671{col 73}{space 3} .2424725
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1592486{col 32}{space 2} .0137504{col 43}{space 1}   11.58{col 52}{space 3}0.000{col 60}{space 4} .1322937{col 73}{space 3} .1862035
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .2081281{col 32}{space 2}  .013568{col 43}{space 1}   15.34{col 52}{space 3}0.000{col 60}{space 4} .1815308{col 73}{space 3} .2347254
{txt}{space 18} {c |}
{space 11}age3150 {c |}{col 20}{res}{space 2}-.0217268{col 32}{space 2} .0102047{col 43}{space 1}   -2.13{col 52}{space 3}0.033{col 60}{space 4}-.0417309{col 73}{space 3}-.0017226
{txt}{space 11}age5165 {c |}{col 20}{res}{space 2}-.0385257{col 32}{space 2} .0108121{col 43}{space 1}   -3.56{col 52}{space 3}0.000{col 60}{space 4}-.0597207{col 73}{space 3}-.0173308
{txt}{space 11}agegt65 {c |}{col 20}{res}{space 2} -.049089{col 32}{space 2} .0126122{col 43}{space 1}   -3.89{col 52}{space 3}0.000{col 60}{space 4}-.0738125{col 73}{space 3}-.0243654
{txt}{space 13}black {c |}{col 20}{res}{space 2}-.0118294{col 32}{space 2} .0081238{col 43}{space 1}   -1.46{col 52}{space 3}0.145{col 60}{space 4}-.0277545{col 73}{space 3} .0040958
{txt}{space 12}latino {c |}{col 20}{res}{space 2}-.0048569{col 32}{space 2}  .016792{col 43}{space 1}   -0.29{col 52}{space 3}0.772{col 60}{space 4}-.0377742{col 73}{space 3} .0280604
{txt}{space 13}other {c |}{col 20}{res}{space 2} .0292979{col 32}{space 2} .0161671{col 43}{space 1}    1.81{col 52}{space 3}0.070{col 60}{space 4}-.0023943{col 73}{space 3} .0609901
{txt}{space 12}female {c |}{col 20}{res}{space 2}-.0277355{col 32}{space 2}  .007535{col 43}{space 1}   -3.68{col 52}{space 3}0.000{col 60}{space 4}-.0425063{col 73}{space 3}-.0129648
{txt}{space 11}college {c |}{col 20}{res}{space 2} .0273867{col 32}{space 2}  .007678{col 43}{space 1}    3.57{col 52}{space 3}0.000{col 60}{space 4} .0123355{col 73}{space 3} .0424379
{txt}{space 18} {c |}
{space 6}inc_quartile {c |}
{space 16}1  {c |}{col 20}{res}{space 2}-.0148441{col 32}{space 2}  .009753{col 43}{space 1}   -1.52{col 52}{space 3}0.128{col 60}{space 4}-.0339629{col 73}{space 3} .0042747
{txt}{space 16}2  {c |}{col 20}{res}{space 2} .0033721{col 32}{space 2} .0102476{col 43}{space 1}    0.33{col 52}{space 3}0.742{col 60}{space 4}-.0167162{col 73}{space 3} .0234605
{txt}{space 16}3  {c |}{col 20}{res}{space 2} .0354955{col 32}{space 2} .0119271{col 43}{space 1}    2.98{col 52}{space 3}0.003{col 60}{space 4} .0121148{col 73}{space 3} .0588761
{txt}{space 18} {c |}
{space 2}looking_for_work {c |}{col 20}{res}{space 2}-.0064601{col 32}{space 2} .0111523{col 43}{space 1}   -0.58{col 52}{space 3}0.562{col 60}{space 4}-.0283219{col 73}{space 3} .0154017
{txt}{space 9}homeowner {c |}{col 20}{res}{space 2} .0140825{col 32}{space 2} .0081356{col 43}{space 1}    1.73{col 52}{space 3}0.083{col 60}{space 4}-.0018656{col 73}{space 3} .0300306
{txt}{space 18} {c |}
{space 5}years_in_msa2 {c |}
{space 16}5  {c |}{col 20}{res}{space 2} .0146578{col 32}{space 2} .0176098{col 43}{space 1}    0.83{col 52}{space 3}0.405{col 60}{space 4}-.0198627{col 73}{space 3} .0491783
{txt}{space 15}10  {c |}{col 20}{res}{space 2} .0001669{col 32}{space 2} .0177063{col 43}{space 1}    0.01{col 52}{space 3}0.992{col 60}{space 4}-.0345427{col 73}{space 3} .0348765
{txt}{space 15}15  {c |}{col 20}{res}{space 2} .0029176{col 32}{space 2} .0120636{col 43}{space 1}    0.24{col 52}{space 3}0.809{col 60}{space 4}-.0207306{col 73}{space 3} .0265657
{txt}{space 18} {c |}
{space 15}msa {c |}
{space 8}Cleveland  {c |}{col 20}{res}{space 2} .0168196{col 32}{space 2} .0141372{col 43}{space 1}    1.19{col 52}{space 3}0.234{col 60}{space 4}-.0108934{col 73}{space 3} .0445326
{txt}{space 10}Houston  {c |}{col 20}{res}{space 2} .0187492{col 32}{space 2} .0149193{col 43}{space 1}    1.26{col 52}{space 3}0.209{col 60}{space 4} -.010497{col 73}{space 3} .0479955
{txt}{space 5}Indianapolis  {c |}{col 20}{res}{space 2} -.008737{col 32}{space 2} .0138835{col 43}{space 1}   -0.63{col 52}{space 3}0.529{col 60}{space 4}-.0359526{col 73}{space 3} .0184787
{txt}{space 10}Memphis  {c |}{col 20}{res}{space 2} .0028626{col 32}{space 2} .0143727{col 43}{space 1}    0.20{col 52}{space 3}0.842{col 60}{space 4}-.0253122{col 73}{space 3} .0310374
{txt}{space 8}Rochester  {c |}{col 20}{res}{space 2} .0363011{col 32}{space 2} .0161133{col 43}{space 1}    2.25{col 52}{space 3}0.024{col 60}{space 4} .0047143{col 73}{space 3} .0678878
{txt}{space 8}St. Louis  {c |}{col 20}{res}{space 2} -.014029{col 32}{space 2}  .013556{col 43}{space 1}   -1.03{col 52}{space 3}0.301{col 60}{space 4}-.0406027{col 73}{space 3} .0125447
{txt}{space 10}Seattle  {c |}{col 20}{res}{space 2}  .010334{col 32}{space 2} .0142922{col 43}{space 1}    0.72{col 52}{space 3}0.470{col 60}{space 4}-.0176829{col 73}{space 3} .0383509
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .0259034{col 32}{space 2} .0182531{col 43}{space 1}    1.42{col 52}{space 3}0.156{col 60}{space 4}-.0098781{col 73}{space 3} .0616848
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,409.50084159002)

Linear regression                               Number of obs     = {res}     7,331
                                                {txt}F(6, 7324)        =  {res}    26.19
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0330
                                                {txt}Root MSE          =    {res} .44025

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}  reduce_trade_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0411258{col 32}{space 2} .0200755{col 43}{space 1}    2.05{col 52}{space 3}0.041{col 60}{space 4} .0017721{col 73}{space 3} .0804796
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2}-.0120762{col 32}{space 2} .0213933{col 43}{space 1}   -0.56{col 52}{space 3}0.572{col 60}{space 4}-.0540132{col 73}{space 3} .0298607
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .0714394{col 32}{space 2} .0203246{col 43}{space 1}    3.51{col 52}{space 3}0.000{col 60}{space 4} .0315973{col 73}{space 3} .1112815
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .1756724{col 32}{space 2} .0273097{col 43}{space 1}    6.43{col 52}{space 3}0.000{col 60}{space 4} .1221375{col 73}{space 3} .2292074
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1536448{col 32}{space 2}   .02322{col 43}{space 1}    6.62{col 52}{space 3}0.000{col 60}{space 4} .1081269{col 73}{space 3} .1991627
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .2117568{col 32}{space 2} .0219712{col 43}{space 1}    9.64{col 52}{space 3}0.000{col 60}{space 4} .1686868{col 73}{space 3} .2548267
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .1939538{col 32}{space 2} .0127963{col 43}{space 1}   15.16{col 52}{space 3}0.000{col 60}{space 4} .1688694{col 73}{space 3} .2190382
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}     7,200
                                                {txt}F(29, 7170)       =  {res}    15.75
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0560
                                                {txt}Root MSE          =    {res}  .4317

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}  reduce_trade_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0571869{col 32}{space 2}  .016219{col 43}{space 1}    3.53{col 52}{space 3}0.000{col 60}{space 4} .0253928{col 73}{space 3} .0889809
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2}-.0061173{col 32}{space 2} .0169394{col 43}{space 1}   -0.36{col 52}{space 3}0.718{col 60}{space 4}-.0393236{col 73}{space 3}  .027089
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2}  .083541{col 32}{space 2} .0167248{col 43}{space 1}    5.00{col 52}{space 3}0.000{col 60}{space 4} .0507554{col 73}{space 3} .1163266
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .1418868{col 32}{space 2} .0218351{col 43}{space 1}    6.50{col 52}{space 3}0.000{col 60}{space 4} .0990835{col 73}{space 3} .1846902
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1252711{col 32}{space 2} .0185435{col 43}{space 1}    6.76{col 52}{space 3}0.000{col 60}{space 4} .0889204{col 73}{space 3} .1616217
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .1882782{col 32}{space 2} .0180747{col 43}{space 1}   10.42{col 52}{space 3}0.000{col 60}{space 4} .1528464{col 73}{space 3} .2237101
{txt}{space 18} {c |}
{space 11}age3150 {c |}{col 20}{res}{space 2} .0459387{col 32}{space 2} .0139623{col 43}{space 1}    3.29{col 52}{space 3}0.001{col 60}{space 4} .0185685{col 73}{space 3} .0733088
{txt}{space 11}age5165 {c |}{col 20}{res}{space 2} .0716961{col 32}{space 2} .0156275{col 43}{space 1}    4.59{col 52}{space 3}0.000{col 60}{space 4} .0410617{col 73}{space 3} .1023305
{txt}{space 11}agegt65 {c |}{col 20}{res}{space 2} .0407555{col 32}{space 2} .0181503{col 43}{space 1}    2.25{col 52}{space 3}0.025{col 60}{space 4} .0051756{col 73}{space 3} .0763354
{txt}{space 13}black {c |}{col 20}{res}{space 2} .0029083{col 32}{space 2} .0160473{col 43}{space 1}    0.18{col 52}{space 3}0.856{col 60}{space 4}-.0285492{col 73}{space 3} .0343658
{txt}{space 12}latino {c |}{col 20}{res}{space 2}-.0263198{col 32}{space 2} .0228131{col 43}{space 1}   -1.15{col 52}{space 3}0.249{col 60}{space 4}-.0710401{col 73}{space 3} .0184006
{txt}{space 13}other {c |}{col 20}{res}{space 2} .0004688{col 32}{space 2} .0199986{col 43}{space 1}    0.02{col 52}{space 3}0.981{col 60}{space 4}-.0387344{col 73}{space 3} .0396719
{txt}{space 12}female {c |}{col 20}{res}{space 2} .0740677{col 32}{space 2} .0104496{col 43}{space 1}    7.09{col 52}{space 3}0.000{col 60}{space 4} .0535834{col 73}{space 3}  .094552
{txt}{space 11}college {c |}{col 20}{res}{space 2}-.0701439{col 32}{space 2} .0113758{col 43}{space 1}   -6.17{col 52}{space 3}0.000{col 60}{space 4}-.0924438{col 73}{space 3}-.0478439
{txt}{space 18} {c |}
{space 6}inc_quartile {c |}
{space 16}1  {c |}{col 20}{res}{space 2} .0076923{col 32}{space 2} .0166456{col 43}{space 1}    0.46{col 52}{space 3}0.644{col 60}{space 4}-.0249381{col 73}{space 3} .0403226
{txt}{space 16}2  {c |}{col 20}{res}{space 2} .0037106{col 32}{space 2} .0165957{col 43}{space 1}    0.22{col 52}{space 3}0.823{col 60}{space 4}-.0288217{col 73}{space 3}  .036243
{txt}{space 16}3  {c |}{col 20}{res}{space 2}-.0362532{col 32}{space 2} .0177696{col 43}{space 1}   -2.04{col 52}{space 3}0.041{col 60}{space 4}-.0710869{col 73}{space 3}-.0014194
{txt}{space 18} {c |}
{space 2}looking_for_work {c |}{col 20}{res}{space 2} -.022336{col 32}{space 2} .0174421{col 43}{space 1}   -1.28{col 52}{space 3}0.200{col 60}{space 4}-.0565276{col 73}{space 3} .0118557
{txt}{space 9}homeowner {c |}{col 20}{res}{space 2}  .013948{col 32}{space 2} .0127227{col 43}{space 1}    1.10{col 52}{space 3}0.273{col 60}{space 4}-.0109922{col 73}{space 3} .0388883
{txt}{space 18} {c |}
{space 5}years_in_msa2 {c |}
{space 16}5  {c |}{col 20}{res}{space 2} .0207492{col 32}{space 2} .0235089{col 43}{space 1}    0.88{col 52}{space 3}0.377{col 60}{space 4}-.0253352{col 73}{space 3} .0668336
{txt}{space 15}10  {c |}{col 20}{res}{space 2} .0206549{col 32}{space 2}  .025699{col 43}{space 1}    0.80{col 52}{space 3}0.422{col 60}{space 4}-.0297226{col 73}{space 3} .0710325
{txt}{space 15}15  {c |}{col 20}{res}{space 2} .0230245{col 32}{space 2}   .01657{col 43}{space 1}    1.39{col 52}{space 3}0.165{col 60}{space 4}-.0094575{col 73}{space 3} .0555065
{txt}{space 18} {c |}
{space 15}msa {c |}
{space 8}Cleveland  {c |}{col 20}{res}{space 2}  .013703{col 32}{space 2} .0213065{col 43}{space 1}    0.64{col 52}{space 3}0.520{col 60}{space 4}-.0280641{col 73}{space 3} .0554701
{txt}{space 10}Houston  {c |}{col 20}{res}{space 2}-.0856163{col 32}{space 2} .0201634{col 43}{space 1}   -4.25{col 52}{space 3}0.000{col 60}{space 4}-.1251425{col 73}{space 3}-.0460902
{txt}{space 5}Indianapolis  {c |}{col 20}{res}{space 2}-.0391105{col 32}{space 2} .0203598{col 43}{space 1}   -1.92{col 52}{space 3}0.055{col 60}{space 4}-.0790217{col 73}{space 3} .0008006
{txt}{space 10}Memphis  {c |}{col 20}{res}{space 2} .0051167{col 32}{space 2} .0212865{col 43}{space 1}    0.24{col 52}{space 3}0.810{col 60}{space 4}-.0366111{col 73}{space 3} .0468445
{txt}{space 8}Rochester  {c |}{col 20}{res}{space 2} .0218142{col 32}{space 2} .0226606{col 43}{space 1}    0.96{col 52}{space 3}0.336{col 60}{space 4}-.0226072{col 73}{space 3} .0662356
{txt}{space 8}St. Louis  {c |}{col 20}{res}{space 2}-.0010698{col 32}{space 2} .0208785{col 43}{space 1}   -0.05{col 52}{space 3}0.959{col 60}{space 4}-.0419978{col 73}{space 3} .0398582
{txt}{space 10}Seattle  {c |}{col 20}{res}{space 2}-.0932627{col 32}{space 2} .0197074{col 43}{space 1}   -4.73{col 52}{space 3}0.000{col 60}{space 4}-.1318951{col 73}{space 3}-.0546304
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .1470385{col 32}{space 2} .0277544{col 43}{space 1}    5.30{col 52}{space 3}0.000{col 60}{space 4} .0926318{col 73}{space 3} .2014453
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,409.50084159002)

Linear regression                               Number of obs     = {res}     7,331
                                                {txt}F(6, 7324)        =  {res}    12.99
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0169
                                                {txt}Root MSE          =    {res} .44677

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}for_investment_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0182463{col 32}{space 2} .0210329{col 43}{space 1}    0.87{col 52}{space 3}0.386{col 60}{space 4}-.0229843{col 73}{space 3} .0594769
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2} .0152792{col 32}{space 2} .0245557{col 43}{space 1}    0.62{col 52}{space 3}0.534{col 60}{space 4}-.0328571{col 73}{space 3} .0634155
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .0352393{col 32}{space 2} .0213752{col 43}{space 1}    1.65{col 52}{space 3}0.099{col 60}{space 4}-.0066622{col 73}{space 3} .0771409
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .1041106{col 32}{space 2} .0265583{col 43}{space 1}    3.92{col 52}{space 3}0.000{col 60}{space 4} .0520487{col 73}{space 3} .1561726
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1336832{col 32}{space 2} .0234682{col 43}{space 1}    5.70{col 52}{space 3}0.000{col 60}{space 4} .0876787{col 73}{space 3} .1796877
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .1489949{col 32}{space 2} .0221494{col 43}{space 1}    6.73{col 52}{space 3}0.000{col 60}{space 4} .1055757{col 73}{space 3} .1924141
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .2247844{col 32}{space 2} .0138348{col 43}{space 1}   16.25{col 52}{space 3}0.000{col 60}{space 4} .1976643{col 73}{space 3} .2519046
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}     7,200
                                                {txt}F(29, 7170)       =  {res}     8.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0332
                                                {txt}Root MSE          =    {res} .44274

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}for_investment_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0522956{col 32}{space 2} .0167248{col 43}{space 1}    3.13{col 52}{space 3}0.002{col 60}{space 4} .0195101{col 73}{space 3} .0850812
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2} .0170222{col 32}{space 2} .0185022{col 43}{space 1}    0.92{col 52}{space 3}0.358{col 60}{space 4}-.0192475{col 73}{space 3} .0532919
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .0629728{col 32}{space 2} .0170034{col 43}{space 1}    3.70{col 52}{space 3}0.000{col 60}{space 4} .0296412{col 73}{space 3} .0963045
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2}  .111659{col 32}{space 2}  .022248{col 43}{space 1}    5.02{col 52}{space 3}0.000{col 60}{space 4} .0680463{col 73}{space 3} .1552717
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .1291706{col 32}{space 2} .0191409{col 43}{space 1}    6.75{col 52}{space 3}0.000{col 60}{space 4} .0916488{col 73}{space 3} .1666924
{txt}Strong Republican  {c |}{col 20}{res}{space 2}  .147402{col 32}{space 2} .0183502{col 43}{space 1}    8.03{col 52}{space 3}0.000{col 60}{space 4} .1114303{col 73}{space 3} .1833737
{txt}{space 18} {c |}
{space 11}age3150 {c |}{col 20}{res}{space 2} .0320186{col 32}{space 2} .0139816{col 43}{space 1}    2.29{col 52}{space 3}0.022{col 60}{space 4} .0046105{col 73}{space 3} .0594266
{txt}{space 11}age5165 {c |}{col 20}{res}{space 2} .0929012{col 32}{space 2} .0158487{col 43}{space 1}    5.86{col 52}{space 3}0.000{col 60}{space 4}  .061833{col 73}{space 3} .1239693
{txt}{space 11}agegt65 {c |}{col 20}{res}{space 2} .0918023{col 32}{space 2} .0187677{col 43}{space 1}    4.89{col 52}{space 3}0.000{col 60}{space 4}  .055012{col 73}{space 3} .1285926
{txt}{space 13}black {c |}{col 20}{res}{space 2} .0145093{col 32}{space 2} .0162461{col 43}{space 1}    0.89{col 52}{space 3}0.372{col 60}{space 4}-.0173378{col 73}{space 3} .0463564
{txt}{space 12}latino {c |}{col 20}{res}{space 2}-.0321734{col 32}{space 2} .0242157{col 43}{space 1}   -1.33{col 52}{space 3}0.184{col 60}{space 4}-.0796433{col 73}{space 3} .0152966
{txt}{space 13}other {c |}{col 20}{res}{space 2}-.0222056{col 32}{space 2} .0204348{col 43}{space 1}   -1.09{col 52}{space 3}0.277{col 60}{space 4}-.0622639{col 73}{space 3} .0178527
{txt}{space 12}female {c |}{col 20}{res}{space 2} .0510275{col 32}{space 2} .0109063{col 43}{space 1}    4.68{col 52}{space 3}0.000{col 60}{space 4}  .029648{col 73}{space 3}  .072407
{txt}{space 11}college {c |}{col 20}{res}{space 2}-.0417526{col 32}{space 2} .0115693{col 43}{space 1}   -3.61{col 52}{space 3}0.000{col 60}{space 4}-.0644318{col 73}{space 3}-.0190734
{txt}{space 18} {c |}
{space 6}inc_quartile {c |}
{space 16}1  {c |}{col 20}{res}{space 2} .0296456{col 32}{space 2} .0166796{col 43}{space 1}    1.78{col 52}{space 3}0.076{col 60}{space 4}-.0030513{col 73}{space 3} .0623425
{txt}{space 16}2  {c |}{col 20}{res}{space 2}  .016653{col 32}{space 2} .0165519{col 43}{space 1}    1.01{col 52}{space 3}0.314{col 60}{space 4}-.0157936{col 73}{space 3} .0490995
{txt}{space 16}3  {c |}{col 20}{res}{space 2}-.0135503{col 32}{space 2} .0180811{col 43}{space 1}   -0.75{col 52}{space 3}0.454{col 60}{space 4}-.0489945{col 73}{space 3}  .021894
{txt}{space 18} {c |}
{space 2}looking_for_work {c |}{col 20}{res}{space 2} .0120147{col 32}{space 2} .0179584{col 43}{space 1}    0.67{col 52}{space 3}0.503{col 60}{space 4}-.0231891{col 73}{space 3} .0472184
{txt}{space 9}homeowner {c |}{col 20}{res}{space 2} .0332784{col 32}{space 2} .0128483{col 43}{space 1}    2.59{col 52}{space 3}0.010{col 60}{space 4} .0080919{col 73}{space 3}  .058465
{txt}{space 18} {c |}
{space 5}years_in_msa2 {c |}
{space 16}5  {c |}{col 20}{res}{space 2} .0024218{col 32}{space 2} .0242863{col 43}{space 1}    0.10{col 52}{space 3}0.921{col 60}{space 4}-.0451866{col 73}{space 3} .0500301
{txt}{space 15}10  {c |}{col 20}{res}{space 2}-.0178602{col 32}{space 2}  .025539{col 43}{space 1}   -0.70{col 52}{space 3}0.484{col 60}{space 4}-.0679241{col 73}{space 3} .0322038
{txt}{space 15}15  {c |}{col 20}{res}{space 2} .0058157{col 32}{space 2} .0169666{col 43}{space 1}    0.34{col 52}{space 3}0.732{col 60}{space 4}-.0274438{col 73}{space 3} .0390753
{txt}{space 18} {c |}
{space 15}msa {c |}
{space 8}Cleveland  {c |}{col 20}{res}{space 2} .0186378{col 32}{space 2} .0209407{col 43}{space 1}    0.89{col 52}{space 3}0.373{col 60}{space 4}-.0224121{col 73}{space 3} .0596877
{txt}{space 10}Houston  {c |}{col 20}{res}{space 2} .0296402{col 32}{space 2} .0210765{col 43}{space 1}    1.41{col 52}{space 3}0.160{col 60}{space 4}-.0116759{col 73}{space 3} .0709564
{txt}{space 5}Indianapolis  {c |}{col 20}{res}{space 2} .0112911{col 32}{space 2} .0205329{col 43}{space 1}    0.55{col 52}{space 3}0.582{col 60}{space 4}-.0289594{col 73}{space 3} .0515416
{txt}{space 10}Memphis  {c |}{col 20}{res}{space 2} .0194645{col 32}{space 2} .0207319{col 43}{space 1}    0.94{col 52}{space 3}0.348{col 60}{space 4}-.0211761{col 73}{space 3} .0601051
{txt}{space 8}Rochester  {c |}{col 20}{res}{space 2} .0157419{col 32}{space 2} .0220425{col 43}{space 1}    0.71{col 52}{space 3}0.475{col 60}{space 4}-.0274679{col 73}{space 3} .0589517
{txt}{space 8}St. Louis  {c |}{col 20}{res}{space 2} .0214354{col 32}{space 2} .0206458{col 43}{space 1}    1.04{col 52}{space 3}0.299{col 60}{space 4}-.0190364{col 73}{space 3} .0619072
{txt}{space 10}Seattle  {c |}{col 20}{res}{space 2} .0298781{col 32}{space 2}  .020729{col 43}{space 1}    1.44{col 52}{space 3}0.150{col 60}{space 4}-.0107568{col 73}{space 3}  .070513
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .1018375{col 32}{space 2} .0278974{col 43}{space 1}    3.65{col 52}{space 3}0.000{col 60}{space 4} .0471503{col 73}{space 3} .1565247
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(sum of wgt is 7,409.50084159002)

Linear regression                               Number of obs     = {res}     7,331
                                                {txt}F(6, 7324)        =  {res}   188.22
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1935
                                                {txt}Root MSE          =    {res} .44272

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}  reduce_immig_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0871166{col 32}{space 2} .0216615{col 43}{space 1}    4.02{col 52}{space 3}0.000{col 60}{space 4} .0446538{col 73}{space 3} .1295794
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2}  .024469{col 32}{space 2} .0240861{col 43}{space 1}    1.02{col 52}{space 3}0.310{col 60}{space 4}-.0227467{col 73}{space 3} .0716847
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .2011738{col 32}{space 2} .0224329{col 43}{space 1}    8.97{col 52}{space 3}0.000{col 60}{space 4} .1571988{col 73}{space 3} .2451487
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .4789551{col 32}{space 2} .0273096{col 43}{space 1}   17.54{col 52}{space 3}0.000{col 60}{space 4} .4254205{col 73}{space 3} .5324897
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .4343533{col 32}{space 2} .0241715{col 43}{space 1}   17.97{col 52}{space 3}0.000{col 60}{space 4} .3869701{col 73}{space 3} .4817365
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .5659741{col 32}{space 2} .0209041{col 43}{space 1}   27.07{col 52}{space 3}0.000{col 60}{space 4} .5249959{col 73}{space 3} .6069522
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .1860875{col 32}{space 2}  .013577{col 43}{space 1}   13.71{col 52}{space 3}0.000{col 60}{space 4} .1594727{col 73}{space 3} .2127024
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}     7,200
                                                {txt}F(29, 7170)       =  {res}    97.47
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2386
                                                {txt}Root MSE          =    {res} .43018

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}  reduce_immig_bin{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pid7 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .1261633{col 32}{space 2} .0160958{col 43}{space 1}    7.84{col 52}{space 3}0.000{col 60}{space 4} .0946107{col 73}{space 3} .1577159
{txt}{space 4}Lean Democrat  {c |}{col 20}{res}{space 2} .0384762{col 32}{space 2} .0173274{col 43}{space 1}    2.22{col 52}{space 3}0.026{col 60}{space 4} .0045095{col 73}{space 3} .0724429
{txt}{space 6}Independent  {c |}{col 20}{res}{space 2} .2394614{col 32}{space 2} .0171997{col 43}{space 1}   13.92{col 52}{space 3}0.000{col 60}{space 4} .2057449{col 73}{space 3}  .273178
{txt}{space 2}Lean Republican  {c |}{col 20}{res}{space 2} .4623337{col 32}{space 2} .0217493{col 43}{space 1}   21.26{col 52}{space 3}0.000{col 60}{space 4} .4196987{col 73}{space 3} .5049688
{txt}{space 7}Republican  {c |}{col 20}{res}{space 2} .4344059{col 32}{space 2} .0187685{col 43}{space 1}   23.15{col 52}{space 3}0.000{col 60}{space 4} .3976142{col 73}{space 3} .4711976
{txt}Strong Republican  {c |}{col 20}{res}{space 2} .5399497{col 32}{space 2} .0167467{col 43}{space 1}   32.24{col 52}{space 3}0.000{col 60}{space 4} .5071213{col 73}{space 3} .5727781
{txt}{space 18} {c |}
{space 11}age3150 {c |}{col 20}{res}{space 2} .0885148{col 32}{space 2} .0142621{col 43}{space 1}    6.21{col 52}{space 3}0.000{col 60}{space 4} .0605569{col 73}{space 3} .1164727
{txt}{space 11}age5165 {c |}{col 20}{res}{space 2} .1528767{col 32}{space 2} .0156904{col 43}{space 1}    9.74{col 52}{space 3}0.000{col 60}{space 4} .1221188{col 73}{space 3} .1836346
{txt}{space 11}agegt65 {c |}{col 20}{res}{space 2}  .147243{col 32}{space 2}  .017613{col 43}{space 1}    8.36{col 52}{space 3}0.000{col 60}{space 4} .1127164{col 73}{space 3} .1817697
{txt}{space 13}black {c |}{col 20}{res}{space 2}-.0437039{col 32}{space 2} .0161422{col 43}{space 1}   -2.71{col 52}{space 3}0.007{col 60}{space 4}-.0753474{col 73}{space 3}-.0120605
{txt}{space 12}latino {c |}{col 20}{res}{space 2}-.0928322{col 32}{space 2} .0245258{col 43}{space 1}   -3.79{col 52}{space 3}0.000{col 60}{space 4}  -.14091{col 73}{space 3}-.0447543
{txt}{space 13}other {c |}{col 20}{res}{space 2}-.0658751{col 32}{space 2} .0202419{col 43}{space 1}   -3.25{col 52}{space 3}0.001{col 60}{space 4}-.1055552{col 73}{space 3} -.026195
{txt}{space 12}female {c |}{col 20}{res}{space 2} .0165602{col 32}{space 2} .0105352{col 43}{space 1}    1.57{col 52}{space 3}0.116{col 60}{space 4}-.0040919{col 73}{space 3} .0372123
{txt}{space 11}college {c |}{col 20}{res}{space 2}-.0914448{col 32}{space 2} .0111695{col 43}{space 1}   -8.19{col 52}{space 3}0.000{col 60}{space 4}-.1133403{col 73}{space 3}-.0695492
{txt}{space 18} {c |}
{space 6}inc_quartile {c |}
{space 16}1  {c |}{col 20}{res}{space 2} .0068291{col 32}{space 2} .0168477{col 43}{space 1}    0.41{col 52}{space 3}0.685{col 60}{space 4}-.0261973{col 73}{space 3} .0398555
{txt}{space 16}2  {c |}{col 20}{res}{space 2}-.0277327{col 32}{space 2} .0164441{col 43}{space 1}   -1.69{col 52}{space 3}0.092{col 60}{space 4}-.0599679{col 73}{space 3} .0045025
{txt}{space 16}3  {c |}{col 20}{res}{space 2}-.0611609{col 32}{space 2} .0176955{col 43}{space 1}   -3.46{col 52}{space 3}0.001{col 60}{space 4}-.0958493{col 73}{space 3}-.0264724
{txt}{space 18} {c |}
{space 2}looking_for_work {c |}{col 20}{res}{space 2}-.0143653{col 32}{space 2} .0175918{col 43}{space 1}   -0.82{col 52}{space 3}0.414{col 60}{space 4}-.0488504{col 73}{space 3} .0201199
{txt}{space 9}homeowner {c |}{col 20}{res}{space 2} .0409197{col 32}{space 2} .0126626{col 43}{space 1}    3.23{col 52}{space 3}0.001{col 60}{space 4} .0160973{col 73}{space 3}  .065742
{txt}{space 18} {c |}
{space 5}years_in_msa2 {c |}
{space 16}5  {c |}{col 20}{res}{space 2} .0014471{col 32}{space 2} .0240733{col 43}{space 1}    0.06{col 52}{space 3}0.952{col 60}{space 4}-.0457437{col 73}{space 3}  .048638
{txt}{space 15}10  {c |}{col 20}{res}{space 2}-.0491602{col 32}{space 2} .0248201{col 43}{space 1}   -1.98{col 52}{space 3}0.048{col 60}{space 4} -.097815{col 73}{space 3}-.0005054
{txt}{space 15}15  {c |}{col 20}{res}{space 2} .0302176{col 32}{space 2}  .017031{col 43}{space 1}    1.77{col 52}{space 3}0.076{col 60}{space 4}-.0031682{col 73}{space 3} .0636034
{txt}{space 18} {c |}
{space 15}msa {c |}
{space 8}Cleveland  {c |}{col 20}{res}{space 2} -.016599{col 32}{space 2} .0205226{col 43}{space 1}   -0.81{col 52}{space 3}0.419{col 60}{space 4}-.0568293{col 73}{space 3} .0236314
{txt}{space 10}Houston  {c |}{col 20}{res}{space 2}-.0191956{col 32}{space 2} .0210324{col 43}{space 1}   -0.91{col 52}{space 3}0.361{col 60}{space 4}-.0604252{col 73}{space 3}  .022034
{txt}{space 5}Indianapolis  {c |}{col 20}{res}{space 2}-.0275791{col 32}{space 2} .0201444{col 43}{space 1}   -1.37{col 52}{space 3}0.171{col 60}{space 4}-.0670681{col 73}{space 3} .0119099
{txt}{space 10}Memphis  {c |}{col 20}{res}{space 2} .0270174{col 32}{space 2} .0204768{col 43}{space 1}    1.32{col 52}{space 3}0.187{col 60}{space 4}-.0131231{col 73}{space 3}  .067158
{txt}{space 8}Rochester  {c |}{col 20}{res}{space 2} -.020408{col 32}{space 2} .0216257{col 43}{space 1}   -0.94{col 52}{space 3}0.345{col 60}{space 4}-.0628008{col 73}{space 3} .0219848
{txt}{space 8}St. Louis  {c |}{col 20}{res}{space 2}-.0386259{col 32}{space 2} .0200778{col 43}{space 1}   -1.92{col 52}{space 3}0.054{col 60}{space 4}-.0779843{col 73}{space 3} .0007324
{txt}{space 10}Seattle  {c |}{col 20}{res}{space 2}-.0595276{col 32}{space 2} .0200327{col 43}{space 1}   -2.97{col 52}{space 3}0.003{col 60}{space 4}-.0987976{col 73}{space 3}-.0202576
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .1303625{col 32}{space 2} .0277736{col 43}{space 1}    4.69{col 52}{space 3}0.000{col 60}{space 4} .0759181{col 73}{space 3} .1848069
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. // coefplot to quickly look at results -- actual plot is made in separate R file
. coefplot (reduce_immig_bin_mult, keep(*pid*) base) ///
>                  (ineq_smaller_bin_mult, keep(*pid*) base) ///
>                  (for_investment_bin_mult, keep(*pid*) base) ///
>                  (reduce_trade_bin_mult, keep(*pid*) base) ///
>                  (safety_net_bin_mult, keep(*pid*) base) , ///
>                  plotlabels("Reduce immigration" "Don't make inequality smaller" "Reduce foreign investment" "Reduce trade" "Reduce safety net") 
{res}{txt}
{com}. *graph export figs/national_policy_polarization.pdf, replace
. 
. 
. // a few other categorical outcomes
. mlogit policy_approach_trade `ind_covs'

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-7561.1071}  
Iteration 1:{space 3}log likelihood = {res:-6856.9763}  
Iteration 2:{space 3}log likelihood = {res:-6841.9742}  
Iteration 3:{space 3}log likelihood = {res:-6841.9034}  
Iteration 4:{space 3}log likelihood = {res:-6841.9033}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}     7,200
{txt}{col 49}LR chi2({res}58{txt}){col 67}= {res}   1438.41
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-6841.9033{txt}{col 49}Pseudo R2{col 67}= {res}    0.0951

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           policy_approach_trade{col 34}{c |}      Coef.{col 46}   Std. Err.{col 58}      z{col 66}   P>|z|{col 74}     [95% Con{col 87}f. Interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}The_federal_government_should_im {txt}{c |}
{space 28}pid7 {c |}
{space 23}Democrat  {c |}{col 34}{res}{space 2} .2988195{col 46}{space 2} .1084607{col 57}{space 1}    2.76{col 66}{space 3}0.006{col 74}{space 4} .0862404{col 87}{space 3} .5113986
{txt}{space 18}Lean Democrat  {c |}{col 34}{res}{space 2} -.346682{col 46}{space 2} .1377319{col 57}{space 1}   -2.52{col 66}{space 3}0.012{col 74}{space 4}-.6166315{col 87}{space 3}-.0767324
{txt}{space 20}Independent  {c |}{col 34}{res}{space 2} .9774411{col 46}{space 2} .1023667{col 57}{space 1}    9.55{col 66}{space 3}0.000{col 74}{space 4}  .776806{col 87}{space 3} 1.178076
{txt}{space 16}Lean Republican  {c |}{col 34}{res}{space 2}  1.80799{col 46}{space 2} .1219253{col 57}{space 1}   14.83{col 66}{space 3}0.000{col 74}{space 4} 1.569021{col 87}{space 3} 2.046959
{txt}{space 21}Republican  {c |}{col 34}{res}{space 2} 1.525263{col 46}{space 2} .1087251{col 57}{space 1}   14.03{col 66}{space 3}0.000{col 74}{space 4} 1.312166{col 87}{space 3}  1.73836
{txt}{space 14}Strong Republican  {c |}{col 34}{res}{space 2} 2.229128{col 46}{space 2} .1073962{col 57}{space 1}   20.76{col 66}{space 3}0.000{col 74}{space 4} 2.018635{col 87}{space 3} 2.439621
{txt}{space 32} {c |}
{space 25}age3150 {c |}{col 34}{res}{space 2} .3521013{col 46}{space 2} .0885793{col 57}{space 1}    3.97{col 66}{space 3}0.000{col 74}{space 4} .1784891{col 87}{space 3} .5257135
{txt}{space 25}age5165 {c |}{col 34}{res}{space 2} .2015092{col 46}{space 2} .0936353{col 57}{space 1}    2.15{col 66}{space 3}0.031{col 74}{space 4} .0179874{col 87}{space 3}  .385031
{txt}{space 25}agegt65 {c |}{col 34}{res}{space 2} .0556881{col 46}{space 2} .1067837{col 57}{space 1}    0.52{col 66}{space 3}0.602{col 74}{space 4}-.1536041{col 87}{space 3} .2649804
{txt}{space 27}black {c |}{col 34}{res}{space 2} .1533063{col 46}{space 2} .0959608{col 57}{space 1}    1.60{col 66}{space 3}0.110{col 74}{space 4}-.0347734{col 87}{space 3}  .341386
{txt}{space 26}latino {c |}{col 34}{res}{space 2}-.2930929{col 46}{space 2} .1493458{col 57}{space 1}   -1.96{col 66}{space 3}0.050{col 74}{space 4}-.5858052{col 87}{space 3}-.0003806
{txt}{space 27}other {c |}{col 34}{res}{space 2} .0982351{col 46}{space 2} .1264897{col 57}{space 1}    0.78{col 66}{space 3}0.437{col 74}{space 4}-.1496801{col 87}{space 3} .3461503
{txt}{space 26}female {c |}{col 34}{res}{space 2} .0873182{col 46}{space 2} .0619577{col 57}{space 1}    1.41{col 66}{space 3}0.159{col 74}{space 4}-.0341167{col 87}{space 3} .2087531
{txt}{space 25}college {c |}{col 34}{res}{space 2} -.580943{col 46}{space 2} .0643196{col 57}{space 1}   -9.03{col 66}{space 3}0.000{col 74}{space 4}-.7070071{col 87}{space 3}-.4548789
{txt}{space 32} {c |}
{space 20}inc_quartile {c |}
{space 30}1  {c |}{col 34}{res}{space 2}  .002406{col 46}{space 2} .0947526{col 57}{space 1}    0.03{col 66}{space 3}0.980{col 74}{space 4}-.1833057{col 87}{space 3} .1881177
{txt}{space 30}2  {c |}{col 34}{res}{space 2}-.0783325{col 46}{space 2} .0941096{col 57}{space 1}   -0.83{col 66}{space 3}0.405{col 74}{space 4} -.262784{col 87}{space 3}  .106119
{txt}{space 30}3  {c |}{col 34}{res}{space 2}-.3829075{col 46}{space 2} .1030325{col 57}{space 1}   -3.72{col 66}{space 3}0.000{col 74}{space 4}-.5848474{col 87}{space 3}-.1809676
{txt}{space 32} {c |}
{space 16}looking_for_work {c |}{col 34}{res}{space 2} -.034621{col 46}{space 2} .1040589{col 57}{space 1}   -0.33{col 66}{space 3}0.739{col 74}{space 4}-.2385726{col 87}{space 3} .1693307
{txt}{space 23}homeowner {c |}{col 34}{res}{space 2} .1288243{col 46}{space 2} .0739844{col 57}{space 1}    1.74{col 66}{space 3}0.082{col 74}{space 4}-.0161825{col 87}{space 3} .2738311
{txt}{space 32} {c |}
{space 19}years_in_msa2 {c |}
{space 30}5  {c |}{col 34}{res}{space 2}  .043757{col 46}{space 2} .1493925{col 57}{space 1}    0.29{col 66}{space 3}0.770{col 74}{space 4}-.2490469{col 87}{space 3} .3365609
{txt}{space 29}10  {c |}{col 34}{res}{space 2} .0821717{col 46}{space 2} .1581483{col 57}{space 1}    0.52{col 66}{space 3}0.603{col 74}{space 4}-.2277933{col 87}{space 3} .3921367
{txt}{space 29}15  {c |}{col 34}{res}{space 2}  .254649{col 46}{space 2} .1054982{col 57}{space 1}    2.41{col 66}{space 3}0.016{col 74}{space 4} .0478763{col 87}{space 3} .4614216
{txt}{space 32} {c |}
{space 29}msa {c |}
{space 22}Cleveland  {c |}{col 34}{res}{space 2} .0212519{col 46}{space 2} .1174449{col 57}{space 1}    0.18{col 66}{space 3}0.856{col 74}{space 4}-.2089358{col 87}{space 3} .2514396
{txt}{space 24}Houston  {c |}{col 34}{res}{space 2}-.1249909{col 46}{space 2} .1210568{col 57}{space 1}   -1.03{col 66}{space 3}0.302{col 74}{space 4}-.3622578{col 87}{space 3}  .112276
{txt}{space 19}Indianapolis  {c |}{col 34}{res}{space 2}-.4405273{col 46}{space 2} .1176078{col 57}{space 1}   -3.75{col 66}{space 3}0.000{col 74}{space 4}-.6710343{col 87}{space 3}-.2100203
{txt}{space 24}Memphis  {c |}{col 34}{res}{space 2}-.2038081{col 46}{space 2} .1174234{col 57}{space 1}   -1.74{col 66}{space 3}0.083{col 74}{space 4}-.4339538{col 87}{space 3} .0263377
{txt}{space 22}Rochester  {c |}{col 34}{res}{space 2} .1138033{col 46}{space 2} .1242192{col 57}{space 1}    0.92{col 66}{space 3}0.360{col 74}{space 4}-.1296618{col 87}{space 3} .3572685
{txt}{space 22}St. Louis  {c |}{col 34}{res}{space 2}-.0239514{col 46}{space 2}  .117461{col 57}{space 1}   -0.20{col 66}{space 3}0.838{col 74}{space 4}-.2541708{col 87}{space 3}  .206268
{txt}{space 24}Seattle  {c |}{col 34}{res}{space 2}-.3135087{col 46}{space 2}   .12285{col 57}{space 1}   -2.55{col 66}{space 3}0.011{col 74}{space 4}-.5542904{col 87}{space 3}-.0727271
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} -1.34518{col 46}{space 2} .1729899{col 57}{space 1}   -7.78{col 66}{space 3}0.000{col 74}{space 4}-1.684234{col 87}{space 3}-1.006126
{txt}{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}The_federal_government_should_en {txt}{c |}
{space 28}pid7 {c |}
{space 23}Democrat  {c |}{col 34}{res}{space 2} .1762985{col 46}{space 2}  .095928{col 57}{space 1}    1.84{col 66}{space 3}0.066{col 74}{space 4} -.011717{col 87}{space 3}  .364314
{txt}{space 18}Lean Democrat  {c |}{col 34}{res}{space 2}-.2436219{col 46}{space 2} .1138431{col 57}{space 1}   -2.14{col 66}{space 3}0.032{col 74}{space 4}-.4667502{col 87}{space 3}-.0204936
{txt}{space 20}Independent  {c |}{col 34}{res}{space 2} .2293959{col 46}{space 2} .1002427{col 57}{space 1}    2.29{col 66}{space 3}0.022{col 74}{space 4} .0329238{col 87}{space 3}  .425868
{txt}{space 16}Lean Republican  {c |}{col 34}{res}{space 2} .3031344{col 46}{space 2} .1448175{col 57}{space 1}    2.09{col 66}{space 3}0.036{col 74}{space 4} .0192973{col 87}{space 3} .5869714
{txt}{space 21}Republican  {c |}{col 34}{res}{space 2} .4034224{col 46}{space 2} .1156174{col 57}{space 1}    3.49{col 66}{space 3}0.000{col 74}{space 4} .1768164{col 87}{space 3} .6300284
{txt}{space 14}Strong Republican  {c |}{col 34}{res}{space 2} .6443375{col 46}{space 2} .1199804{col 57}{space 1}    5.37{col 66}{space 3}0.000{col 74}{space 4} .4091802{col 87}{space 3} .8794949
{txt}{space 32} {c |}
{space 25}age3150 {c |}{col 34}{res}{space 2}-.1956904{col 46}{space 2} .0801848{col 57}{space 1}   -2.44{col 66}{space 3}0.015{col 74}{space 4}-.3528496{col 87}{space 3}-.0385311
{txt}{space 25}age5165 {c |}{col 34}{res}{space 2}-.8309576{col 46}{space 2} .0939775{col 57}{space 1}   -8.84{col 66}{space 3}0.000{col 74}{space 4} -1.01515{col 87}{space 3}-.6467651
{txt}{space 25}agegt65 {c |}{col 34}{res}{space 2}-1.237014{col 46}{space 2} .1221409{col 57}{space 1}  -10.13{col 66}{space 3}0.000{col 74}{space 4}-1.476406{col 87}{space 3}-.9976225
{txt}{space 27}black {c |}{col 34}{res}{space 2} .1654148{col 46}{space 2} .0931974{col 57}{space 1}    1.77{col 66}{space 3}0.076{col 74}{space 4}-.0172488{col 87}{space 3} .3480784
{txt}{space 26}latino {c |}{col 34}{res}{space 2} .0672076{col 46}{space 2} .1354582{col 57}{space 1}    0.50{col 66}{space 3}0.620{col 74}{space 4}-.1982855{col 87}{space 3} .3327007
{txt}{space 27}other {c |}{col 34}{res}{space 2}  .167091{col 46}{space 2}  .120516{col 57}{space 1}    1.39{col 66}{space 3}0.166{col 74}{space 4} -.069116{col 87}{space 3} .4032979
{txt}{space 26}female {c |}{col 34}{res}{space 2} -.054067{col 46}{space 2} .0654786{col 57}{space 1}   -0.83{col 66}{space 3}0.409{col 74}{space 4}-.1824027{col 87}{space 3} .0742687
{txt}{space 25}college {c |}{col 34}{res}{space 2}-.2605572{col 46}{space 2} .0687187{col 57}{space 1}   -3.79{col 66}{space 3}0.000{col 74}{space 4}-.3952434{col 87}{space 3} -.125871
{txt}{space 32} {c |}
{space 20}inc_quartile {c |}
{space 30}1  {c |}{col 34}{res}{space 2}-.1807193{col 46}{space 2} .0977485{col 57}{space 1}   -1.85{col 66}{space 3}0.064{col 74}{space 4}-.3723028{col 87}{space 3} .0108641
{txt}{space 30}2  {c |}{col 34}{res}{space 2}-.1904189{col 46}{space 2} .0969865{col 57}{space 1}   -1.96{col 66}{space 3}0.050{col 74}{space 4}-.3805089{col 87}{space 3} -.000329
{txt}{space 30}3  {c |}{col 34}{res}{space 2}-.3155148{col 46}{space 2}  .106585{col 57}{space 1}   -2.96{col 66}{space 3}0.003{col 74}{space 4}-.5244175{col 87}{space 3} -.106612
{txt}{space 32} {c |}
{space 16}looking_for_work {c |}{col 34}{res}{space 2} -.094571{col 46}{space 2} .1032366{col 57}{space 1}   -0.92{col 66}{space 3}0.360{col 74}{space 4} -.296911{col 87}{space 3} .1077689
{txt}{space 23}homeowner {c |}{col 34}{res}{space 2} -.036827{col 46}{space 2} .0749294{col 57}{space 1}   -0.49{col 66}{space 3}0.623{col 74}{space 4}-.1836859{col 87}{space 3}  .110032
{txt}{space 32} {c |}
{space 19}years_in_msa2 {c |}
{space 30}5  {c |}{col 34}{res}{space 2}-.1287677{col 46}{space 2} .1444006{col 57}{space 1}   -0.89{col 66}{space 3}0.373{col 74}{space 4}-.4117876{col 87}{space 3} .1542521
{txt}{space 29}10  {c |}{col 34}{res}{space 2} .0544212{col 46}{space 2} .1538803{col 57}{space 1}    0.35{col 66}{space 3}0.724{col 74}{space 4}-.2471787{col 87}{space 3} .3560211
{txt}{space 29}15  {c |}{col 34}{res}{space 2} .1029496{col 46}{space 2} .0990018{col 57}{space 1}    1.04{col 66}{space 3}0.298{col 74}{space 4}-.0910903{col 87}{space 3} .2969895
{txt}{space 32} {c |}
{space 29}msa {c |}
{space 22}Cleveland  {c |}{col 34}{res}{space 2}-.0105782{col 46}{space 2} .1281157{col 57}{space 1}   -0.08{col 66}{space 3}0.934{col 74}{space 4}-.2616804{col 87}{space 3}  .240524
{txt}{space 24}Houston  {c |}{col 34}{res}{space 2} .1383152{col 46}{space 2} .1256182{col 57}{space 1}    1.10{col 66}{space 3}0.271{col 74}{space 4}-.1078919{col 87}{space 3} .3845223
{txt}{space 19}Indianapolis  {c |}{col 34}{res}{space 2}-.2305655{col 46}{space 2} .1262763{col 57}{space 1}   -1.83{col 66}{space 3}0.068{col 74}{space 4}-.4780625{col 87}{space 3} .0169316
{txt}{space 24}Memphis  {c |}{col 34}{res}{space 2}-.0980618{col 46}{space 2} .1263019{col 57}{space 1}   -0.78{col 66}{space 3}0.438{col 74}{space 4} -.345609{col 87}{space 3} .1494854
{txt}{space 22}Rochester  {c |}{col 34}{res}{space 2} .0661305{col 46}{space 2} .1356686{col 57}{space 1}    0.49{col 66}{space 3}0.626{col 74}{space 4}-.1997751{col 87}{space 3}  .332036
{txt}{space 22}St. Louis  {c |}{col 34}{res}{space 2}-.0175695{col 46}{space 2} .1270582{col 57}{space 1}   -0.14{col 66}{space 3}0.890{col 74}{space 4} -.266599{col 87}{space 3} .2314601
{txt}{space 24}Seattle  {c |}{col 34}{res}{space 2}-.0642702{col 46}{space 2}  .125183{col 57}{space 1}   -0.51{col 66}{space 3}0.608{col 74}{space 4}-.3096243{col 87}{space 3} .1810839
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2}-.2027916{col 46}{space 2} .1643869{col 57}{space 1}   -1.23{col 66}{space 3}0.217{col 74}{space 4} -.524984{col 87}{space 3} .1194008
{txt}{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_eq_3                           {col 34}{txt}{c |}  (base outcome)
{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. test i2.pid7 i3.pid7  i4.pid7 i5.pid7 i6.pid7 i7.pid7 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[The_federal_government_should_im]2.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [The_federal_government_should_en]2.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [_eq_3]2o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [The_federal_government_should_im]3.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [The_federal_government_should_en]3.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} [_eq_3]3o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} [The_federal_government_should_im]4.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} [The_federal_government_should_en]4.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} [_eq_3]4o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} [The_federal_government_should_im]5.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} [The_federal_government_should_en]5.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} [_eq_3]5o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} [The_federal_government_should_im]6.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} [The_federal_government_should_en]6.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} [_eq_3]6o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(16)}{space 1} [The_federal_government_should_im]7.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(17)}{space 1} [The_federal_government_should_en]7.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(18)}{space 1} [_eq_3]7o.pid7 = 0{p_end}
{txt}       Constraint 3 dropped
       Constraint 6 dropped
       Constraint 9 dropped
       Constraint 12 dropped
       Constraint 15 dropped
       Constraint 18 dropped

{col 12}chi2( 12) ={res}  740.98
{txt}{col 10}Prob > chi2 =  {res}  0.0000
{txt}
{com}. 
. mlogit policy_approach_tech `ind_covs'

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-6939.1619}  
Iteration 1:{space 3}log likelihood = {res:-6585.7692}  
Iteration 2:{space 3}log likelihood = {res:-6579.6873}  
Iteration 3:{space 3}log likelihood = {res:-6579.6683}  
Iteration 4:{space 3}log likelihood = {res:-6579.6683}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}     7,200
{txt}{col 49}LR chi2({res}58{txt}){col 67}= {res}    718.99
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-6579.6683{txt}{col 49}Pseudo R2{col 67}= {res}    0.0518

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            policy_approach_tech{col 34}{c |}      Coef.{col 46}   Std. Err.{col 58}      z{col 66}   P>|z|{col 74}     [95% Con{col 87}f. Interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}The_federal_government_should_im {txt}{c |}
{space 28}pid7 {c |}
{space 23}Democrat  {c |}{col 34}{res}{space 2} .1278791{col 46}{space 2} .1102797{col 57}{space 1}    1.16{col 66}{space 3}0.246{col 74}{space 4}-.0882652{col 87}{space 3} .3440234
{txt}{space 18}Lean Democrat  {c |}{col 34}{res}{space 2}-.4833671{col 46}{space 2} .1441109{col 57}{space 1}   -3.35{col 66}{space 3}0.001{col 74}{space 4}-.7658193{col 87}{space 3} -.200915
{txt}{space 20}Independent  {c |}{col 34}{res}{space 2} .3894223{col 46}{space 2} .1076994{col 57}{space 1}    3.62{col 66}{space 3}0.000{col 74}{space 4} .1783354{col 87}{space 3} .6005092
{txt}{space 16}Lean Republican  {c |}{col 34}{res}{space 2} .5370833{col 46}{space 2} .1345741{col 57}{space 1}    3.99{col 66}{space 3}0.000{col 74}{space 4} .2733228{col 87}{space 3} .8008438
{txt}{space 21}Republican  {c |}{col 34}{res}{space 2} .4713112{col 46}{space 2} .1187091{col 57}{space 1}    3.97{col 66}{space 3}0.000{col 74}{space 4} .2386455{col 87}{space 3} .7039768
{txt}{space 14}Strong Republican  {c |}{col 34}{res}{space 2} 1.037236{col 46}{space 2} .1093274{col 57}{space 1}    9.49{col 66}{space 3}0.000{col 74}{space 4} .8229579{col 87}{space 3} 1.251514
{txt}{space 32} {c |}
{space 25}age3150 {c |}{col 34}{res}{space 2}-.1769466{col 46}{space 2} .0858249{col 57}{space 1}   -2.06{col 66}{space 3}0.039{col 74}{space 4}-.3451603{col 87}{space 3}-.0087328
{txt}{space 25}age5165 {c |}{col 34}{res}{space 2}-.7800278{col 46}{space 2} .0963263{col 57}{space 1}   -8.10{col 66}{space 3}0.000{col 74}{space 4}-.9688239{col 87}{space 3}-.5912316
{txt}{space 25}agegt65 {c |}{col 34}{res}{space 2}-1.161066{col 46}{space 2} .1187016{col 57}{space 1}   -9.78{col 66}{space 3}0.000{col 74}{space 4}-1.393717{col 87}{space 3}-.9284151
{txt}{space 27}black {c |}{col 34}{res}{space 2} .3243512{col 46}{space 2} .0982083{col 57}{space 1}    3.30{col 66}{space 3}0.001{col 74}{space 4} .1318665{col 87}{space 3}  .516836
{txt}{space 26}latino {c |}{col 34}{res}{space 2} .0595679{col 46}{space 2}  .146969{col 57}{space 1}    0.41{col 66}{space 3}0.685{col 74}{space 4}-.2284861{col 87}{space 3}  .347622
{txt}{space 27}other {c |}{col 34}{res}{space 2} .0060844{col 46}{space 2} .1331838{col 57}{space 1}    0.05{col 66}{space 3}0.964{col 74}{space 4} -.254951{col 87}{space 3} .2671197
{txt}{space 26}female {c |}{col 34}{res}{space 2}-.0277315{col 46}{space 2} .0667505{col 57}{space 1}   -0.42{col 66}{space 3}0.678{col 74}{space 4}-.1585601{col 87}{space 3} .1030971
{txt}{space 25}college {c |}{col 34}{res}{space 2}-.5696351{col 46}{space 2} .0695976{col 57}{space 1}   -8.18{col 66}{space 3}0.000{col 74}{space 4}-.7060439{col 87}{space 3}-.4332264
{txt}{space 32} {c |}
{space 20}inc_quartile {c |}
{space 30}1  {c |}{col 34}{res}{space 2}-.1608584{col 46}{space 2}  .095498{col 57}{space 1}   -1.68{col 66}{space 3}0.092{col 74}{space 4} -.348031{col 87}{space 3} .0263142
{txt}{space 30}2  {c |}{col 34}{res}{space 2} -.398671{col 46}{space 2}  .097021{col 57}{space 1}   -4.11{col 66}{space 3}0.000{col 74}{space 4}-.5888287{col 87}{space 3}-.2085134
{txt}{space 30}3  {c |}{col 34}{res}{space 2}-.5809046{col 46}{space 2} .1086843{col 57}{space 1}   -5.34{col 66}{space 3}0.000{col 74}{space 4}-.7939219{col 87}{space 3}-.3678872
{txt}{space 32} {c |}
{space 16}looking_for_work {c |}{col 34}{res}{space 2} .0324879{col 46}{space 2} .1048298{col 57}{space 1}    0.31{col 66}{space 3}0.757{col 74}{space 4}-.1729748{col 87}{space 3} .2379506
{txt}{space 23}homeowner {c |}{col 34}{res}{space 2} -.020345{col 46}{space 2} .0769839{col 57}{space 1}   -0.26{col 66}{space 3}0.792{col 74}{space 4}-.1712307{col 87}{space 3} .1305407
{txt}{space 32} {c |}
{space 19}years_in_msa2 {c |}
{space 30}5  {c |}{col 34}{res}{space 2}-.3271331{col 46}{space 2} .1527732{col 57}{space 1}   -2.14{col 66}{space 3}0.032{col 74}{space 4}-.6265631{col 87}{space 3}-.0277032
{txt}{space 29}10  {c |}{col 34}{res}{space 2}-.6126342{col 46}{space 2} .1749258{col 57}{space 1}   -3.50{col 66}{space 3}0.000{col 74}{space 4}-.9554825{col 87}{space 3}-.2697858
{txt}{space 29}15  {c |}{col 34}{res}{space 2}-.1294136{col 46}{space 2} .1018117{col 57}{space 1}   -1.27{col 66}{space 3}0.204{col 74}{space 4}-.3289607{col 87}{space 3} .0701336
{txt}{space 32} {c |}
{space 29}msa {c |}
{space 22}Cleveland  {c |}{col 34}{res}{space 2}-.1490674{col 46}{space 2}  .126472{col 57}{space 1}   -1.18{col 66}{space 3}0.239{col 74}{space 4} -.396948{col 87}{space 3} .0988133
{txt}{space 24}Houston  {c |}{col 34}{res}{space 2}-.2001772{col 46}{space 2} .1250719{col 57}{space 1}   -1.60{col 66}{space 3}0.109{col 74}{space 4}-.4453137{col 87}{space 3} .0449593
{txt}{space 19}Indianapolis  {c |}{col 34}{res}{space 2}-.4875255{col 46}{space 2} .1265461{col 57}{space 1}   -3.85{col 66}{space 3}0.000{col 74}{space 4}-.7355513{col 87}{space 3}-.2394997
{txt}{space 24}Memphis  {c |}{col 34}{res}{space 2}-.1431491{col 46}{space 2} .1226709{col 57}{space 1}   -1.17{col 66}{space 3}0.243{col 74}{space 4}-.3835797{col 87}{space 3} .0972815
{txt}{space 22}Rochester  {c |}{col 34}{res}{space 2}-.1421733{col 46}{space 2} .1328647{col 57}{space 1}   -1.07{col 66}{space 3}0.285{col 74}{space 4}-.4025834{col 87}{space 3} .1182367
{txt}{space 22}St. Louis  {c |}{col 34}{res}{space 2}-.0635352{col 46}{space 2} .1236253{col 57}{space 1}   -0.51{col 66}{space 3}0.607{col 74}{space 4}-.3058363{col 87}{space 3}  .178766
{txt}{space 24}Seattle  {c |}{col 34}{res}{space 2}-.2037074{col 46}{space 2} .1285464{col 57}{space 1}   -1.58{col 66}{space 3}0.113{col 74}{space 4}-.4556537{col 87}{space 3} .0482388
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} -.086335{col 46}{space 2} .1704538{col 57}{space 1}   -0.51{col 66}{space 3}0.613{col 74}{space 4}-.4204182{col 87}{space 3} .2477482
{txt}{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}The_federal_government_should_en {txt}{c |}
{space 28}pid7 {c |}
{space 23}Democrat  {c |}{col 34}{res}{space 2}  .007045{col 46}{space 2} .0998716{col 57}{space 1}    0.07{col 66}{space 3}0.944{col 74}{space 4}-.1886998{col 87}{space 3} .2027898
{txt}{space 18}Lean Democrat  {c |}{col 34}{res}{space 2}-.2154325{col 46}{space 2}  .116213{col 57}{space 1}   -1.85{col 66}{space 3}0.064{col 74}{space 4}-.4432058{col 87}{space 3} .0123408
{txt}{space 20}Independent  {c |}{col 34}{res}{space 2} .1690536{col 46}{space 2} .1001105{col 57}{space 1}    1.69{col 66}{space 3}0.091{col 74}{space 4}-.0271594{col 87}{space 3} .3652667
{txt}{space 16}Lean Republican  {c |}{col 34}{res}{space 2} .1544808{col 46}{space 2} .1293514{col 57}{space 1}    1.19{col 66}{space 3}0.232{col 74}{space 4}-.0990434{col 87}{space 3} .4080049
{txt}{space 21}Republican  {c |}{col 34}{res}{space 2} .2160139{col 46}{space 2} .1101246{col 57}{space 1}    1.96{col 66}{space 3}0.050{col 74}{space 4} .0001736{col 87}{space 3} .4318541
{txt}{space 14}Strong Republican  {c |}{col 34}{res}{space 2} .3085254{col 46}{space 2} .1089841{col 57}{space 1}    2.83{col 66}{space 3}0.005{col 74}{space 4} .0949204{col 87}{space 3} .5221303
{txt}{space 32} {c |}
{space 25}age3150 {c |}{col 34}{res}{space 2}-.2050393{col 46}{space 2} .0806155{col 57}{space 1}   -2.54{col 66}{space 3}0.011{col 74}{space 4}-.3630427{col 87}{space 3}-.0470359
{txt}{space 25}age5165 {c |}{col 34}{res}{space 2}-.8806046{col 46}{space 2} .0925021{col 57}{space 1}   -9.52{col 66}{space 3}0.000{col 74}{space 4}-1.061905{col 87}{space 3}-.6993039
{txt}{space 25}agegt65 {c |}{col 34}{res}{space 2}-1.231864{col 46}{space 2} .1157353{col 57}{space 1}  -10.64{col 66}{space 3}0.000{col 74}{space 4}-1.458701{col 87}{space 3}-1.005027
{txt}{space 27}black {c |}{col 34}{res}{space 2} .2513866{col 46}{space 2} .0936344{col 57}{space 1}    2.68{col 66}{space 3}0.007{col 74}{space 4} .0678665{col 87}{space 3} .4349067
{txt}{space 26}latino {c |}{col 34}{res}{space 2} .1626861{col 46}{space 2} .1382578{col 57}{space 1}    1.18{col 66}{space 3}0.239{col 74}{space 4}-.1082942{col 87}{space 3} .4336663
{txt}{space 27}other {c |}{col 34}{res}{space 2} .1627801{col 46}{space 2} .1191834{col 57}{space 1}    1.37{col 66}{space 3}0.172{col 74}{space 4}-.0708151{col 87}{space 3} .3963752
{txt}{space 26}female {c |}{col 34}{res}{space 2}-.1593095{col 46}{space 2} .0633743{col 57}{space 1}   -2.51{col 66}{space 3}0.012{col 74}{space 4}-.2835209{col 87}{space 3}-.0350982
{txt}{space 25}college {c |}{col 34}{res}{space 2} -.226176{col 46}{space 2} .0665414{col 57}{space 1}   -3.40{col 66}{space 3}0.001{col 74}{space 4}-.3565948{col 87}{space 3}-.0957573
{txt}{space 32} {c |}
{space 20}inc_quartile {c |}
{space 30}1  {c |}{col 34}{res}{space 2}  -.07032{col 46}{space 2} .0971129{col 57}{space 1}   -0.72{col 66}{space 3}0.469{col 74}{space 4}-.2606579{col 87}{space 3} .1200178
{txt}{space 30}2  {c |}{col 34}{res}{space 2}-.0758732{col 46}{space 2} .0954567{col 57}{space 1}   -0.79{col 66}{space 3}0.427{col 74}{space 4}-.2629648{col 87}{space 3} .1112184
{txt}{space 30}3  {c |}{col 34}{res}{space 2} -.173058{col 46}{space 2} .1052033{col 57}{space 1}   -1.64{col 66}{space 3}0.100{col 74}{space 4}-.3792527{col 87}{space 3} .0331368
{txt}{space 32} {c |}
{space 16}looking_for_work {c |}{col 34}{res}{space 2} .0141718{col 46}{space 2} .1024629{col 57}{space 1}    0.14{col 66}{space 3}0.890{col 74}{space 4}-.1866519{col 87}{space 3} .2149955
{txt}{space 23}homeowner {c |}{col 34}{res}{space 2}-.1966997{col 46}{space 2} .0733175{col 57}{space 1}   -2.68{col 66}{space 3}0.007{col 74}{space 4}-.3403993{col 87}{space 3}-.0530001
{txt}{space 32} {c |}
{space 19}years_in_msa2 {c |}
{space 30}5  {c |}{col 34}{res}{space 2}-.0223554{col 46}{space 2} .1446417{col 57}{space 1}   -0.15{col 66}{space 3}0.877{col 74}{space 4} -.305848{col 87}{space 3} .2611371
{txt}{space 29}10  {c |}{col 34}{res}{space 2}-.0017317{col 46}{space 2} .1540957{col 57}{space 1}   -0.01{col 66}{space 3}0.991{col 74}{space 4}-.3037536{col 87}{space 3} .3002903
{txt}{space 29}15  {c |}{col 34}{res}{space 2}  .119901{col 46}{space 2} .1018971{col 57}{space 1}    1.18{col 66}{space 3}0.239{col 74}{space 4}-.0798136{col 87}{space 3} .3196156
{txt}{space 32} {c |}
{space 29}msa {c |}
{space 22}Cleveland  {c |}{col 34}{res}{space 2} .2971582{col 46}{space 2} .1209139{col 57}{space 1}    2.46{col 66}{space 3}0.014{col 74}{space 4} .0601713{col 87}{space 3} .5341451
{txt}{space 24}Houston  {c |}{col 34}{res}{space 2}-.0431397{col 46}{space 2} .1256082{col 57}{space 1}   -0.34{col 66}{space 3}0.731{col 74}{space 4}-.2893272{col 87}{space 3} .2030478
{txt}{space 19}Indianapolis  {c |}{col 34}{res}{space 2}-.1876686{col 46}{space 2} .1242119{col 57}{space 1}   -1.51{col 66}{space 3}0.131{col 74}{space 4}-.4311194{col 87}{space 3} .0557822
{txt}{space 24}Memphis  {c |}{col 34}{res}{space 2}-.0273321{col 46}{space 2}  .124231{col 57}{space 1}   -0.22{col 66}{space 3}0.826{col 74}{space 4}-.2708203{col 87}{space 3} .2161561
{txt}{space 22}Rochester  {c |}{col 34}{res}{space 2} .0052155{col 46}{space 2} .1322214{col 57}{space 1}    0.04{col 66}{space 3}0.969{col 74}{space 4}-.2539336{col 87}{space 3} .2643647
{txt}{space 22}St. Louis  {c |}{col 34}{res}{space 2} .0913664{col 46}{space 2} .1236462{col 57}{space 1}    0.74{col 66}{space 3}0.460{col 74}{space 4}-.1509756{col 87}{space 3} .3337084
{txt}{space 24}Seattle  {c |}{col 34}{res}{space 2} .0780188{col 46}{space 2} .1234914{col 57}{space 1}    0.63{col 66}{space 3}0.528{col 74}{space 4}-.1640199{col 87}{space 3} .3200574
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2}-.3553643{col 46}{space 2} .1671669{col 57}{space 1}   -2.13{col 66}{space 3}0.034{col 74}{space 4}-.6830055{col 87}{space 3}-.0277232
{txt}{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_eq_3                           {col 34}{txt}{c |}  (base outcome)
{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. test i2.pid7 i3.pid7  i4.pid7 i5.pid7 i6.pid7 i7.pid7 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[The_federal_government_should_im]2.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [The_federal_government_should_en]2.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [_eq_3]2o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [The_federal_government_should_im]3.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [The_federal_government_should_en]3.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} [_eq_3]3o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} [The_federal_government_should_im]4.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} [The_federal_government_should_en]4.pid7 = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} [_eq_3]4o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} [The_federal_government_should_im]5.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} [The_federal_government_should_en]5.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} [_eq_3]5o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} [The_federal_government_should_im]6.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} [The_federal_government_should_en]6.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} [_eq_3]6o.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(16)}{space 1} [The_federal_government_should_im]7.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(17)}{space 1} [The_federal_government_should_en]7.pid7 = 0{p_end}
{p 0 7}{space 1}{text:(18)}{space 1} [_eq_3]7o.pid7 = 0{p_end}
{txt}       Constraint 3 dropped
       Constraint 6 dropped
       Constraint 9 dropped
       Constraint 12 dropped
       Constraint 15 dropped
       Constraint 18 dropped

{col 12}chi2( 12) ={res}  156.29
{txt}{col 10}Prob > chi2 =  {res}  0.0000
{txt}
{com}. 
. 
. // Save coefficients that are stored in "b" matrix 
. clear
{txt}
{com}. getmata (depvar beta se) = b, force
{res}{txt}
{com}. lab def depvar 1 "Don't make inequality smaller" 2 "Reduce safety net" 3 "Reduce trade"  4 "Reduce foreign investment" 5 "Reduce immigration"
{txt}
{com}. lab val depvar depvar
{txt}
{com}. decode depvar, gen(depvar2) 
{txt}
{com}. drop depvar
{txt}
{com}. rename depvar2 depvar
{res}{txt}
{com}. outsheet using "polarization_coefs.csv", replace comma
{txt}
{com}. 
{txt}end of do-file

{com}. 
. * creates plot seen in fig 1. R code, can also be run outside Stata
. cd $localdir
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files
{txt}
{com}. shell $rexec --vanilla <"$localdir/Code/polarization_natissues_coefplot.R"
{txt}

R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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> # make a coef plot for the polarization figure
> 
> library(ggplot2)
> library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

> 
> # this package contains some functions for making coefplots in ggplot2
> if (!require(wpmarble)){c -(}
+   devtools::install_github("wpmarble/wpmarble")
+   library(wpmarble)
+ {c )-}
Loading required package: wpmarble
> theme_set(theme_bw())
> 
> d = read.csv("Data/polarization_coefs.csv", stringsAsFactors = F)
> d = d %>% arrange(beta)
> d$depvar = factor(d$depvar, d$depvar)
> ggplot(d) + 
+   aes(y = depvar, x = beta, xmin = beta - 1.96 * se, xmax = beta + 1.96 * se) + 
+   geom_errorbarh(height = 0) + 
+   geom_point() + 
+   coord_cartesian(xlim = c(0, .55)) + 
+   scale_x_continuous(breaks = seq(0, 1, .1)) + 
+   labs(x = "Strong Republican - Strong Democrat Difference", y = NULL) + 
+   theme(panel.grid.minor = element_blank())
> ggsave("Output/fig1.pdf", width=6,height=4)
> ggsave("Output/fig1.eps", width=6,height=4)
> 
> # ggsave("figs/national_polarization_coefplot.pdf", width = 6, height=4)  
> # ggsave("../PaperDrafts/ResearchPaper1/Graphs/national_polarization_coefplot.pdf", width=6,height=4)
> 

{com}. 
. 
. * remove stray file created by the R script
. rm "$localdir/Rplots.pdf"
{txt}
{com}. 
. 
. ******* Cleaning code for hierarchical model *******
. do "$localdir/Code/hlm/01prepare_data.do"
{txt}
{com}. // Random effects estimation of the 8 cities conjoint
. 
. cd $localdir
{res}/Users/wpmarble/Dropbox/Cities/Publication_Files
{txt}
{com}. 
. use Data/all_conjoint.dta, clear
{txt}( )

{com}. 
. 
. // create indicators for levels of conjoint
. foreach v of varlist educ hieduc invest gov workers local {c -(}
{txt}  2{com}.         levelsof `v'
{txt}  3{com}.         foreach i of numlist 1/`=`r(r)'-1' {c -(}
{txt}  4{com}.                 gen `v'_ind_`i' = 0
{txt}  5{com}.                 replace `v'_ind_`i' = 1 if `v' == `i'
{txt}  6{com}.         {c )-}
{txt}  7{com}. {c )-}
{res}{txt}1 2 3 4 5
(15,602 real changes made)
(15,491 real changes made)
(15,362 real changes made)
(15,920 real changes made)
{res}{txt}1 2 3 4 5
(15,576 real changes made)
(15,525 real changes made)
(15,715 real changes made)
(15,378 real changes made)
{res}{txt}1 2 3 4
(19,509 real changes made)
(19,592 real changes made)
(19,322 real changes made)
{res}{txt}1 2 3
(26,085 real changes made)
(26,052 real changes made)
{res}{txt}1 2 3 4 5
(15,512 real changes made)
(15,456 real changes made)
(15,476 real changes made)
(15,860 real changes made)
{res}{txt}1 2 3 4
(19,381 real changes made)
(19,514 real changes made)
(19,508 real changes made)

{com}. 
. // save output for R
. sort caseid table plan
{txt}
{com}. save "Data/conjoint_for_r.dta", replace
{txt}file Data/conjoint_for_r.dta saved

{com}. 
{txt}end of do-file

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/wpmarble/Dropbox/Cities/Publication_Files/Output/Results1.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}25 Jul 2020, 20:33:32
{txt}{.-}
{smcl}
{txt}{sf}{ul off}